Using subject sequencing data and a database of therapy biomarker distributions to determine therapy impact

ABSTRACT

Techniques for generating therapy biomarker scores and visualizing same. The techniques include determining, using a patient&#39;s sequence data and distributions of biomarker values across one or more reference populations, a first set of normalized scores for a first set of biomarkers associated with a first therapy, and a second set of normalized scores for a second set of biomarkers associated with a second therapy, generating a graphical user interface (GUI) including a first portion associated with the first therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the first set of normalized scores; and a second portion associated with a second therapy and having at least one visual characteristic determined based on a normalized score of the respective biomarker in the second set of normalized scores; and displaying the generated GUI.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 and is acontinuation of U.S. application Ser. No. 16/662,280, entitled “USINGCANCER OR PRE-CANCER SUBJECT SEQUENCING DATA AND A DATABASE OF THERAPYBIOMARKER DISTRIBUTIONS TO DETERMINE NORMALIZED BIOMARKER SCORES ANDGENERATE A GRAPHICAL USER INTERFACE,” filed Oct. 24, 2019, AttorneyDocket No. B1462.70004US06, which claims priority under 35 U.S.C. § 120and is a continuation of U.S. application Ser. No. 16/456,370, entitled“USING CANCER OR PRE-CANCER SUBJECT SEQUENCING DATA AND A DATABASE OFTHERAPY BIOMARKER DISTRIBUTIONS TO DETERMINE NORMALIZED BIOMARKER SCORESAND GENERATE A GRAPHICAL USER INTERFACE,” filed Jun. 28, 2019, AttorneyDocket No. B1462.70004US04, which claims priority under 35 U.S.C. § 120and is a continuation of U.S. application Ser. No. 16/006,279, entitled“SYSTEMS AND METHODS FOR IDENTIFYING CANCER TREATMENTS FROM NORMALIZEDBIOMARKER SCORES,” filed Jun. 12, 2018, Attorney Docket No.B1462.70004US01, which claims the benefit under 35 U.S.C. § 119(e) ofthe filing date of U.S. provisional patent application Ser. No.62/518,787, entitled “SYSTEMS AND METHODS FOR IDENTIFYING CANCERTREATMENTS FROM SEQUENCE DATA”, filed Jun. 13, 2017, Attorney Docket No.B1462.70001US00 and U.S. provisional patent application Ser. No.62/598,440, entitled “SYSTEMS AND METHODS IDENTIFYING CANCER TREATMENTSFROM SEQUENCE DATA,” filed Dec. 13, 2017, Attorney Docket No.B1462.70002US00, the entire contents of each of which are incorporatedherein by reference.

U.S. application Ser. No. 16/006,279, entitled “SYSTEMS AND METHODS FORIDENTIFYING CANCER TREATMENTS FROM NORMALIZED BIOMARKER SCORES,” filedJun. 12, 2018, Attorney Docket No. B1462.70004US01 was filed on the sameday as International Application No.: PCT/US18/37017, entitled “SYSTEMSAND METHODS FOR GENERATING, VISUALIZING AND CLASSIFYING MOLECULARFUNCTIONAL PROFILES”, bearing Attorney Docket No. B1462.70002WO00;International Application No.: PCT/US18/37018, entitled “SYSTEMS ANDMETHODS FOR IDENTIFYING RESPONDERS AND NON-RESPONDERS TO IMMUNECHECKPOINT BLOCKADE THERAPY”, bearing Attorney Docket No.B1462.70003WO00; and International Application No.: PCT/US18/37008,entitled “SYSTEMS AND METHODS FOR IDENTIFYING CANCER TREATMENTS FROMNORMALIZED BIOMARKER SCORES”, bearing Attorney Docket No.B1462.70004WO00, the entire contents of each of which are incorporatedherein by reference.

FIELD

Aspects of the technology described herein relate to predictingtreatment efficacy based on subject (e.g., patient) specific informationsuch as a subject's (e.g., patient's) biomarkers.

Some aspects of the technology described herein relate to determiningtherapy scores (for one or more potential treatments) and determiningtherapy scores before and after a treatment. Some aspects of thetechnology described herein relate to generating a graphical userinterface (GUI) for visualizing therapy scores.

Some aspects of the technology described herein relate to determiningimpact scores (for treatments). Some aspects of the technology describedherein relate to generating a graphical user interface for visualizingimpact scores.

Some aspects of the technology described herein relate to determiningnormalized biomarker scores for a subject. Some aspects of thetechnology described herein relate to identifying the subject as amember of one or more cohorts using normalized biomarkers scores. Someaspects of the technology described herein relate to outputting suchinformation (e.g., to one or more users). Some aspects of the technologydescribed herein relate to potential inclusion or exclusion of a subjectfrom a clinical trial.

BACKGROUND

Correctly selecting one or more effective therapies for a subject (e.g.,a patient) with cancer or determining the effectiveness of a treatmentcan be crucial for the survival and overall wellbeing of that subject.Advances in identifying effective therapies and understanding theireffectiveness or otherwise aiding in personalized care of patients withcancer are needed.

SUMMARY

Provided herein, inter alia, are systems and methods for determiningtherapy scores for multiple therapies based on normalized biomarkerscores. Such information, in some embodiments, is output to a user in agraphical user interface (GUI).

Systems and methods for determining therapy scores for multipletherapies based on normalized biomarker scores comprises, in someembodiments, accessing sequence data for a subject, accessing biomarkerinformation indicating distribution of values for biomarkers associatedwith multiple therapies, determining normalized biomarker scores for thesubject using sequencing data and biomarker information, and determiningtherapy scores for the multiple therapies based on normalized biomarkerscores.

Provided herein, inter alia, are systems and methods for determiningimpact score for a candidate therapy using first and second normalizedbiomarker scores. Such information, in some embodiments, is output to auser in a graphical user interface (GUI).

Systems and methods for determining impact score for a candidate therapyusing first and second normalized biomarker scores comprises, in someembodiments, obtaining first sequencing data for a subject prior toadministration of candidate therapy, obtaining second sequencing datafor a subject subsequent to administration of candidate therapy,accessing biomarker information indicating distribution of values for abiomarker associated with the candidate therapy, determining first andsecond biomarker scores for the subject using first sequencing data,second sequencing data, and biomarker information, and determiningimpact score for the candidate therapy using first and second normalizedbiomarker scores.

Provided herein, inter alia, are systems and methods for determiningtherapy scores for at least two selected therapies based on normalizedbiomarker scores for the at least three biomarkers. Such information, insome embodiments, is output to a user in a graphical user interface(GUI).

Systems and methods for determining therapy scores for at least twoselected therapies based on normalized biomarker scores for the at leastthree biomarkers comprises, in some embodiments, obtaining sequencingdata for a subject, accessing biomarker information for at least threebiomarkers associated with at least two selected therapies, determiningfirst and second sets of normalized biomarker scores for the subjectusing sequencing data and biomarker information, and determining therapyscores for the at least two selected therapies based on normalizedbiomarker scores for the at least three biomarkers.

Provided herein, inter alia, are systems and methods for obtaining firstand second therapy scores for first and second therapies. Suchinformation, in some embodiments, is output to a user in a graphicaluser interface (GUI).

Systems and methods for obtaining first and second therapy scores forfirst and second therapies comprises, in some embodiments, obtainingsequence data for a subject, accessing biomarker information indicatingdistribution of values for biomarkers associated with multipletherapies, determining first and second sets of normalized biomarkerscores for the subject using sequencing data and biomarker information,and obtaining first and second therapy scores for first and secondtherapies.

Provided herein, inter alia, are systems and methods for identifying asubject as a member of a cohort using normalized biomarker scores. Suchinformation, in some embodiments, is output to a user in a graphicaluser interface (GUI).

Systems and methods for identifying a subject as a member of a cohortusing normalized biomarker scores comprises, in some embodiments,obtaining sequencing data for a subject, accessing biomarker informationindicating distribution of values for biomarkers associated withmultiple therapies, determining normalized biomarker scores for thesubject using sequencing data and biomarker information, and identifyingthe subject as a member of a cohort using normalized biomarker scores.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker in at least a reference subset of a plurality ofbiomarkers across a respective group of people, each of the plurality ofbiomarkers being associated with at least one therapy in a plurality oftherapies; determining, using the sequencing data and the biomarkerinformation, a normalized score for each biomarker in at least a subjectsubset of the plurality of biomarkers to obtain a set of normalizedbiomarker scores for the subject, wherein the subject subset of theplurality of biomarkers is a subset of the reference subset of theplurality of biomarkers; and determining, using the set of normalizedbiomarker scores for the subject, therapy scores for the plurality oftherapies, each of the therapy scores indicative of predicted responseof the subject to administration of a respective therapy in theplurality of therapies.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker in at least areference subset of a plurality of biomarkers across a respective groupof people, each of the plurality of biomarkers being associated with atleast one therapy in a plurality of therapies; determining, using thesequencing data and the biomarker information, a normalized score foreach biomarker in at least a subject subset of the plurality ofbiomarkers to obtain a set of normalized biomarker scores for thesubject, wherein the subject subset of the plurality of biomarkers is asubset of the reference subset of the plurality of biomarkers; anddetermining, using the set of normalized biomarker scores for thesubject, therapy scores for the plurality of therapies, each of thetherapy scores indicative of predicted response of the subject toadministration of a respective therapy in the plurality of therapies.

In one aspect provided herein is a method, comprising using at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker in at least a reference subset of a plurality ofbiomarkers across a respective group of people, each of the plurality ofbiomarkers being associated with at least one therapy in a plurality oftherapies; determining, using the sequencing data and the biomarkerinformation, a normalized score for each biomarker in at least a subjectsubset of the plurality of biomarkers to obtain a set of normalizedbiomarker scores for the subject, wherein the subject subset of theplurality of biomarkers is a subset of the reference subset of theplurality of biomarkers; and determining, using the set of normalizedbiomarker scores for the subject, therapy scores for the plurality oftherapies, each of the therapy scores indicative of predicted responseof the subject to administration of a respective therapy in theplurality of therapies.

In one aspect provided herein is a system comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining first sequencing dataabout at least one biological sample of a subject prior toadministration of a candidate therapy; obtaining second sequencing dataabout at least one other biological sample of the subject subsequent toadministration of the candidate therapy; accessing, in the at least onedatabase, biomarker information indicating a distribution of values foreach biomarker, across a respective group of people, in at least areference subset of a plurality of biomarkers; determining, using thefirst and second sequencing data and the biomarker information, a firstset of normalized biomarker scores for the subject and a second set ofnormalized biomarker scores for the subject; and determining, using thefirst and second sets of normalized biomarker scores for the subject, animpact score for the candidate therapy, wherein the impact score isindicative of response of the subject to administration of the candidatetherapy.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining first sequencing data about at least one biologicalsample of a subject prior to administration of a candidate therapy;obtaining second sequencing data about at least one other biologicalsample of the subject subsequent to administration of the candidatetherapy; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker, across arespective group of people, in at least a reference subset of aplurality of biomarkers; determining, using the first and secondsequencing data and the biomarker information, a first set of normalizedbiomarker scores for the subject and a second set of normalizedbiomarker scores for the subject; and determining, using the first andsecond sets of normalized biomarker scores for the subject, an impactscore for the candidate therapy, wherein the impact score is indicativeof response of the subject to administration of the candidate therapy.

In one aspect provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining first sequencingdata about at least one biological sample of a subject prior toadministration of a candidate therapy; obtaining second sequencing dataabout at least one other biological sample of the subject subsequent toadministration of the candidate therapy; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker, across a respective group of people, in at least areference subset of a plurality of biomarkers; determining, using thefirst and second sequencing data and the biomarker information, a firstset of normalized biomarker scores for the subject and a second set ofnormalized biomarker scores for the subject; and determining, using thefirst and second sets of normalized biomarker scores for the subject, animpact score for the candidate therapy, wherein the impact score isindicative of response of the subject to administration of the candidatetherapy.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker in at least a reference subset of a plurality ofbiomarkers across a respective group of people, each of the plurality ofbiomarkers being associated with at least one therapy in a plurality oftherapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized scores for a first set ofbiomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized scores for a second set ofbiomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalized scoresas input to a statistical model to obtain a first therapy score for thefirst therapy; providing the second set of normalized scores as input tothe statistical model to obtain a second therapy score for the secondtherapy; generating a graphical user interface (GUI), wherein the GUIcomprises: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker in at least areference subset of a plurality of biomarkers across a respective groupof people, each of the plurality of biomarkers being associated with atleast one therapy in a plurality of therapies; determining, using thesequencing data and the biomarker information: a first set of normalizedscores for a first set of biomarkers associated with a first therapy inthe plurality of therapies; and a second set of normalized scores for asecond set of biomarkers associated with a second therapy in theplurality of therapies, wherein the first set of biomarkers is differentfrom the second set of biomarkers; providing the first set of normalizedscores as input to a statistical model to obtain a first therapy scorefor the first therapy; providing the second set of normalized scores asinput to the statistical model to obtain a second therapy score for thesecond therapy; generating a graphical user interface (GUI), wherein theGUI comprises: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In one aspect provided herein is a method, comprising using the at leastone computer hardware processor to perform: obtaining sequencing dataabout at least one biological sample of a subject; accessing, in atleast one database, biomarker information indicating a distribution ofvalues for each biomarker in at least a reference subset of a pluralityof biomarkers across a respective group of people, each of the pluralityof biomarkers being associated with at least one therapy in a pluralityof therapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized scores for a first set ofbiomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized scores for a second set ofbiomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalized scoresas input to a statistical model to obtain a first therapy score for thefirst therapy; providing the second set of normalized scores as input tothe statistical model to obtain a second therapy score for the secondtherapy; generating a graphical user interface (GUI), wherein the GUIcomprises: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker, across a respective group of people, in at least areference subset of the plurality of biomarkers, each of the pluralityof biomarkers being associated with at least one therapy in a pluralityof therapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized biomarker scores for a first setof biomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized biomarker scores for a secondset of biomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalizedbiomarker scores as input to a statistical model to obtain a firsttherapy score for the first therapy; providing the second set ofnormalized biomarker scores as input to the statistical model to obtaina second therapy score for the second therapy; wherein the plurality oftherapies comprise at least two therapies selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, and an anti-CD20 therapy, and wherein theplurality of biomarkers associated with each of the plurality oftherapies comprises at least three biomarkers selected from the group ofbiomarkers associated with the respective therapy in Table 2.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker, across arespective group of people, in at least a reference subset of theplurality of biomarkers, each of the plurality of biomarkers beingassociated with at least one therapy in a plurality of therapies;determining, using the sequencing data and the biomarker information: afirst set of normalized biomarker scores for a first set of biomarkersassociated with a first therapy in the plurality of therapies; and asecond set of normalized biomarker scores for a second set of biomarkersassociated with a second therapy in the plurality of therapies, whereinthe first set of biomarkers is different from the second set ofbiomarkers; providing the first set of normalized biomarker scores asinput to a statistical model to obtain a first therapy score for thefirst therapy; providing the second set of normalized biomarker scoresas input to the statistical model to obtain a second therapy score forthe second therapy; wherein the plurality of therapies comprise at leasttwo therapies selected from the group consisting of: an anti-PD1therapy, an anti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy,an anti-cancer vaccine therapy, an anti-angiogenic therapy, and ananti-CD20 therapy, and wherein the plurality of biomarkers associatedwith each of the plurality of therapies comprises at least threebiomarkers selected from the group of biomarkers associated with therespective therapy in Table 2.

In one aspect provided herein is a method, comprising using at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker, across a respective group of people, in at least areference subset of the plurality of biomarkers, each of the pluralityof biomarkers being associated with at least one therapy in a pluralityof therapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized biomarker scores for a first setof biomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized biomarker scores for a secondset of biomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalizedbiomarker scores as input to a statistical model to obtain a firsttherapy score for the first therapy; providing the second set ofnormalized biomarker scores as input to the statistical model to obtaina second therapy score for the second therapy; wherein the plurality oftherapies comprise at least two therapies selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, and an anti-CD20 therapy, and wherein theplurality of biomarkers associated with each of the plurality oftherapies comprises at least three biomarkers selected from the group ofbiomarkers associated with the respective therapy in Table 2.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker, across a respective group of people, in at least areference subset of the plurality of biomarkers, each of the pluralityof biomarkers being associated with at least one candidate therapy;determining, using the sequencing data and the biomarker information, anormalized score for each biomarker in at least a subject subset of theplurality of biomarkers to obtain a set of normalized biomarkers for thesubject; identifying the subject as a member of one or more cohortsbased on the set of normalized biomarker scores for the subject, whereineach of the one or more cohorts is associated with a positive ornegative outcome of the at least one candidate therapy; and outputtingan indication of the one or more cohorts in which the subject is amember.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker, across arespective group of people, in at least a reference subset of theplurality of biomarkers, each of the plurality of biomarkers beingassociated with at least one candidate therapy; determining, using thesequencing data and the biomarker information, a normalized score foreach biomarker in at least a subject subset of the plurality ofbiomarkers to obtain a set of normalized biomarkers for the subject;identifying the subject as a member of one or more cohorts based on theset of normalized biomarker scores for the subject, wherein each of theone or more cohorts is associated with a positive or negative outcome ofthe at least one candidate therapy; and outputting an indication of theone or more cohorts in which the subject is a member.

In one aspect a method comprising using at least one computer hardwareprocessor to perform: obtaining sequencing data about at least onebiological sample of a subject; accessing, in at least one database,biomarker information indicating a distribution of values for eachbiomarker, across a respective group of people, in at least a referencesubset of the plurality of biomarkers, each of the plurality ofbiomarkers being associated with at least one candidate therapy;determining, using the sequencing data and the biomarker information, anormalized score for each biomarker in at least a subject subset of theplurality of biomarkers to obtain a set of normalized biomarkers for thesubject; identifying the subject as a member of one or more cohortsbased on the set of normalized biomarker scores for the subject, whereineach of the one or more cohorts is associated with a positive ornegative outcome of the at least one candidate therapy; and outputtingan indication of the one or more cohorts in which the subject is amember.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and embodiments will be described with reference to thefollowing figures. The figures are not necessarily drawn to scale.

FIG. 1A is a diagram of an illustrative process for obtaining patientdata and providing that data to a doctor, in accordance with someembodiments of the technology described herein.

FIG. 1B is a block diagram of patient data that may be presented to auser, in accordance with some embodiments of the technology describedherein.

FIG. 1C is a graphical representation of patient data that may bepresented to a user, in accordance with some embodiments of thetechnology described herein.

FIG. 2A is a flow chart of an illustrative process for determiningtherapy scores for multiple therapies based normalized biomarker scores,in accordance with some embodiments of the technology described herein.

FIG. 2B is a flow chart of an illustrative process for determiningimpact score for a candidate therapy using a first normalized biomarkerscore and a second normalized biomarker score, in accordance with someembodiments of the technology described herein.

FIG. 2C is a flow chart of an illustrative process for determiningtherapy scores for that at least two selected therapies based onnormalized biomarker scores for the at least three biomarkers, inaccordance with some embodiments of the technology described herein.

FIG. 2D is a flow chart of an illustrative process for obtaining firstand second therapy scores for first and second therapies, in accordancewith some embodiments of the technology described herein.

FIG. 2E is a flow chart of an illustrative process for identifying asubject as a member of a cohort using normalized biomarker scores, inaccordance with some embodiments of the technology described herein.

FIG. 3 is a graphical representation of biomarker value distribution fora large patient cohort, as determined in accordance with someembodiments of the technology described herein.

FIG. 4 is a graphical representation of patient therapy scorescalculated as the sum of positive and negative biomarkers, in accordancewith some embodiments of the technology described herein.

FIG. 5 is a graphical representation of patient therapy scorescalculated for multiple therapies for a patient that has been determinedas responsive (Patient 1) or non-responsive (Patient 2) to an anti-PD1therapy (Pembrolizumab), in accordance with some embodiments of thetechnology described herein.

FIG. 6A is a screenshot presenting normalized biomarker valuescalculated for different immunotherapies, in accordance with someembodiments of the technology described herein.

FIG. 6B is a screenshot presenting patient therapy scores for differentimmunotherapies calculated using normalized biomarker values, inaccordance with some embodiments of the technology described herein.

FIG. 6C is a screenshot presenting information related to biomarkersused to calculate patient therapy scores, in accordance with someembodiments of the technology described herein.

FIG. 7A is a graphical representation of therapy scores calculated forpatients treated with an anti-PD1 therapy (Pembrolizumab), in accordancewith some embodiments of the technology described herein. Patients withprogressive disease (PD) are shown in red, patients with stable disease(SD) are shown in light blue, and patients with complete response (CR)are shown in blue.

FIG. 7B is a graphical representation of therapy scores calculated forpatients treated with an anti-CTLA4 therapy (Ipililumab), in accordancewith some embodiments of the technology described herein. Patients withprogressive disease (PD) are shown as a dark solid line, patients withstable disease (SD) are shown as a light grey striped line, and patientswith partial response (PR) are shown in a dark grey striped line.

FIG. 7C is a graphical representation of therapy scores calculated forpatients treated with an anti-PD1 therapy (Pembrolizumab), in accordancewith some embodiments of the technology described herein. Patients withprogressive disease (PD) are shown as a dark solid line, patients withstable disease (SD) are shown as a light grey striped line, and patientswith partial response (PR) are shown in a dark grey striped line.

FIG. 8A is a graphical representation of therapy scores calculatedwithout additional weight optimization in a machine learning-basedoptimization of biomarker importance, in accordance with someembodiments of the technology described herein. Patients withprogressive disease (PD) are shown as a dark solid line, patients withstable disease (SD) are shown as a light grey striped line, and patientswith partial response (PR) are shown in a dark grey striped line.

FIG. 8B is a graphical representation of therapy scores calculated withmachine-adapted weights, in accordance with some embodiments of thetechnology described herein. Patients with progressive disease (PD) areshown as a dark solid line, patients with stable disease (SD) are shownas a light grey striped line, and patients with partial response (PR)are shown in a dark grey striped line.

FIG. 8C is a graphical representation of biomarker importance in termsof feature importance calculated with forest regression algorithms, inaccordance with some embodiments of the technology described herein.

FIG. 8D is a graphical representation of biomarker weights recalculatedwith a logistic regression model to improve prediction of therapyresponse, in accordance with some embodiments of the technologydescribed herein.

FIG. 9 is a graphic illustrating different types of screens that may beshown to a user of the software program.

FIG. 10 is a screenshot presenting the selected patient's reportincluding information related to the patient's sequencing data, thepatient, and the patient's cancer.

FIG. 11 is a screenshot presenting information related to anti-PD1immunotherapy provided in response to selecting anti-PD1 immunotherapy(as shown by highlighting) in the immunotherapy biomarkers portion ofthe screen (as shown in the left panel).

FIG. 12 is a screenshot presenting selection of mutational burdenbiomarker by a user.

FIG. 13 is a screenshot presenting information relating to themutational burden biomarker (as shown in the middle panel) provided inresponse to the user selecting the mutational burden biomarker.

FIG. 14 is a screenshot presenting clinical trial data relating toanti-PD1 therapy effectivity in patients having stage IV metastaticmelanoma (as shown in the right panel) provided in response to the userselecting anti-PD1 immunotherapy (as shown in the left panel).

FIG. 15 is a block diagram of an illustrative computer system that maybe used in implementing some embodiments of the technology describedherein.

DETAILED DESCRIPTION

Currently, certain conventional therapy selection methods allow forselection of a therapy based on a single parameter (or biomarker) of anindividual patient or tumor, the presence or absence of which iscorrelated with treatment response or patient survival. The inventorshave appreciated that there are several problems with this type ofsingle-parameter methodology. The first problem of such conventionaltherapy selection methods is their weak predictive power when evaluatingpotential candidate therapies. While a particular individual biomarkermay be predictive of the efficacy of a candidate therapy for one cohort(or group) of subjects (e.g., patients), it may fail to do so for asecond or further cohorts (or groups). A second biomarker may bepredictive of the efficacy of the candidate therapy for a second orfurther cohorts (or groups) of subjects (e.g., patients), but fail to doso for the first cohort (or group). Thus, different individualbiomarkers may suggest different courses of action. As a result, using asingle biomarker to determine the efficacy of a candidate treatment isproblematic for many patients. Even if a single biomarker having thehighest correlation with response for a candidate treatment were chosen,it may still have a weak predictive capability without taking intoaccount the full scope of each patient's case and personal condition.

Another problem with conventional single-parameter methodology is theheterogeneity of a biomarker's values. Due to the variation inmeasurements of different clinics and clinical trials, potentialbiomarkers become incomparable between subjects (e.g., patients) fromdifferent hospitals or clinical settings. The biomarker values definedin one study could significantly differ from the results of the samemeasurements performed at a different site or on different equipment.While the relative meaning of a biomarker may remain unchanged—forexample, a “high” biomarker value is bad or “low” value is good forpredicting therapy efficacy—experimental cut-off or threshold values for“high” or “low” definitions often significantly vary among studies.

The inventors have developed techniques for predicting the efficacy oftherapies for a subject that address (e.g., mitigate or avoid) theabove-described problems of conventional single-biomarker approaches. Inparticular, the inventors have developed techniques of predictingtherapy efficacy using multiple biomarkers (e.g., biomarkers associatedwith positive therapeutic response or non-positive therapeutic responseto a particular therapy or type of therapy). The inventors haveappreciated that different biomarkers may have values in vastlydifferent ranges. In order to use multiple such biomarkers in a singlecommon quantitative framework for predicting therapy efficacy, theinventors have developed a technique for normalizing the values of thebiomarkers relative to their variation in reference populations, therebyplacing them on a common scale. The inventors have also recognized thatcomparing biomarker scores of a patient to those of other patients maybe used to compute normalized biomarker scores. Further, such normalizedbiomarker scores may be utilized to more accurately predict a patient'sresponse to a therapy. The inventors have specifically developedtechniques for simultaneous analysis of the normalized biomarkers asdescribed herein.

Additionally, recent advances in personalized genomic sequencing andcancer genomic sequencing technologies have made it possible to obtainpatient-specific information about cancer cells (e.g., tumor cells) andcancer microenvironments from one or more biological samples obtainedfrom individual patients. This information can be used to determine alarge number of parameters (or biomarkers) for each patient and,potentially, use this information to identify effective therapies and/orselect one or more effective therapies for the subject (e.g., thepatient). This information may also be used to determine how a subject(e.g., a patient) is responding over time to a treatment and, ifnecessary, to select a new therapy or therapies for the subject (e.g.,the patient) as necessary. This information may also be used todetermine whether the subject (e.g., the patient) should be included orexcluded from participating in a clinical trial.

Global comparison of different types and groupings of biomarkers usingnormalization as described herein was not known in the art, and theintegration of such normalized biomarkers in a coherent and quantitativemanner with therapy or impact scores calculated therefrom provide moreaccurate predictions (greater predictive capacity) of a patient'sresponse to a therapy than might be seen by the use of any single markeror less complex combination of elements. The methods, systems, andgraphical user interfaces (GUIs) based on such a wide variety ofbiomarkers as described herein are newly available and not previouslydescribed techniques or methods existed to perform the elements of thesetechniques. Further, techniques for combining various types ofbiomarkers in a single analytical tool had not been developed becausethese biomarkers were from different origins (i.e., different studies,hospitals, and treatment centers) and were of vastly differing scales.

The inventors have recognized that several of the elements describedherein add something more than what is well understood, routine, orconventional activity proposed by others in the field. These meaningfulnon-routine steps result in the improvements seen in the methods,systems, and GUIs described herein and include, but are not limited to:the normalization of different biomarker types to a common scale; thecombination(s) of biomarker types provided herein; the determination oftherapy scores from different biomarker types; technical improvements inanalyses that allow for more accurate prediction of a patient's responseto a therapy and resulting improvements in outcome for the patient; andthe creation of improved graphical user interfaces to aid in theselection of a therapy.

Therefore, aspects of the technology described herein relate to systemsand methods and for predicting a patient's response to a therapy basedon patient specific information such as a patient's biomarker values. Insome embodiments, predicting a patient's response to a therapy comprisesdetermining normalized biomarker scores (also described as “normalizedscores”) using sequencing data and biomarker information. In someembodiments, predicting a patient's response to a therapy comprisesdetermining therapy scores for the multiple therapies based onnormalized biomarker scores. A therapy score for a therapy is anumerical value that may provide a quantitative measure of the therapy'spredicted efficacy in treating a subject. In some embodiments,determining a patient's response to a therapy comprises determining animpact score based on normalized biomarker scores. An impact score for atherapy is a numerical value that may provide a quantitative measure ofthe therapy's current efficacy (impact) in treating a subject.

Such methods and systems may be useful for clinical purposes including,for example, selecting a treatment, evaluating suitability of a patientfor participating in a clinical trial, or determining a course oftreatment for a subject (e.g., a patient).

The methods and systems described herein may also be useful fornon-clinical applications including (as a non-limiting example) researchpurposes such as, e.g., studying the biological pathways and/orbiological processes targeted by a therapy, and developing new therapiesfor cancer based on such studies.

Further, systems which present this information in a comprehensive anduseable format will be needed to facilitate treatment of patients withsuch conditions. Therefore, provided herein are systems and methods foranalyzing patient specific information that result in a prediction of apatient's response or lack thereof to a treatment.

Such an analysis takes into consideration a global view of patientinformation to make a prediction regarding the patient's response to atherapy that is well-informed and comprehensive. The analysis describedherein, in some embodiments, is a global analysis of patient specificinformation. Certain aspects of the described methods take into accountbiological data generated from analysis of at least one biologicalsample of a subject. Other aspects of the described methods take intoaccount patient specific information related to the overall healthand/or lifestyle of a patient (e.g., personal habits, environmentalfactors) that may play a role in whether a patient responds to atherapy.

Generally, techniques described herein provide for improvements overconventional computer-implemented techniques for analysis of medicaldata such as evaluation of expression data (e.g., RNA expression data)and determining whether one or more therapies (e.g., targeted therapies,radiotherapies, and/or immunotherapies) will be effective in treatingthe subject. Such improvements include, but are not limited to,improvements in predictive power regarding the effectiveness ofcandidate treatments for a subject over conventional single biomarkertreatments. Additionally, some embodiments of the technology providedherein are directed to graphical user interfaces that presentoncological data in a new way which is compact and highly informative.These graphical user interfaces not only reduce the cognitive load on auser (e.g., a doctor or other medical professional) working with them,but may serve to reduce clinician errors and improve the functionalityof a computer by providing all needed information in a singleinteractive interface. This could eliminate the need for a user (e.g., aclinician) to consult different sources of information (e.g., viewmultiple different webpages, use multiple different applicationprograms, etc.), which would otherwise place an additional burden on theprocessing, memory, and communications resources of the computer(s) usedby such a user (e.g., a clinician).

Biomarkers

The methods described are based on in part on the analysis ofanthropometric, clinical, tumor, and/or cancerous cell microenvironmentparameters, and tumor and/or cancerous cell parameters of a subject(e.g., a patient), along with accompanying disease information. For suchanalyses, sequence data such as that from transcriptome, exome, and/orgenome sequencing of a patient's tumor biopsy, or from other tissues ofthe patient are suitable although any type of sequence data may be used.Additional data concerning other patient, cancerous cell, or tumorparameters, or microenvironment parameters may also be consideredincluding, but not limited to: tumor and/or cancerous cell proteomicanalysis; immunohistochemistry staining; flow cytometry; standardclinical measurements of blood, urine and other biological fluids;biopsies of one or more tumors and organs; images obtained by anymethods, including X-ray, ultrasonic, sonic, or magnetic resonanceimaging scintillation studies, etc. In these terms, all features thatdistinguish one patient from another including, but not limited to,disease stage, sex, age, tumor mutations, cancerous cell mutations,blood analysis, IHC of biopsy, etc. are called patient parameters andmay be included in the algorithm. The parameters of the subject (e.g.,the patient), the type of tumor, or the type of cancerous cell may havebeen identified in group clinical trials that were published inscientific journals or actively used in clinically approved analyses,guidelines of treatment options (FDA, NIH, NCCN, CPIC, etc.) orelsewhere. These parameters are biomarkers, the presence or absence ofwhich and/or levels of which may be statistically significantlycorrelated (e.g., the correlation may be at least a threshold amountaway from zero) with treatment response or patient survival.

Certain techniques described herein are designed to use any reliable andavailable information about discovered biomarkers to simultaneouslyanalyze individual biomarkers of the patient and may use any number ofpre-defined biomarker combinations. This method generally considersseveral parameters concerning the patient and/or the cancerous tissuesand/or cells of the patient and does not classify the patient to aone-biomarker group, such as high or low PDL1 expression. Certaintechniques described herein may be based on the simultaneous analysis oftens or hundreds of biomarkers.

In some embodiments, the techniques described herein provide a way togenerate “thresholds” for pre-defined biomarkers based on (e.g., largevolumes of) data obtained from large numbers of patients, such as TCGA,ICGC, Human Protein Atlas, etc., allowing for the creation of anormalized score for each of the biomarkers. Combinations of normalizedbiomarker scores for the patient may be used to analyze one more definedtherapies (creating therapy scores) providing information that allowsthe selection of one or more therapies for each patient based on theirpersonal parameters.

Types of Biomarkers

Aspects of the present disclosure relate to systems and methods forpredicting efficacy of a cancer treatment from a plurality ofbiomarkers. As used herein, the term “biomarker” refers to anyinformation (or any parameter) of a biomolecule (e.g., a gene or aprotein), a cancer (e.g., tumor type) or a subject (e.g., age of asubject) that may be used to predict an effect of a therapy or lackthereof in the subject. Accordingly, “biomarker information” or“biomarker value” as used herein, refers to any information relating toa biomarker. As a non-limiting example, if a biomarker is age, abiomarker value (e.g., information about the biomarker) may be 32 for apatient that is 32 years of age.

A biomarker as described herein may be associated with at least onetherapy and/or at least one cancer. As used herein, the term “associatedwith” indicates that a biomarker has been found to be relevant (e.g., inone or more studies such as those described in a paper or journalarticle) to and/or involved with the associated therapy and/or theassociated cancer. It should be appreciated that a biomarker, in someembodiments, may be directly linked to a therapy and/or cancer orindirectly linked to a therapy and/or a cancer (e.g., that the biomarkerhas been found to directly or indirectly effect or modulate a biologicalprocess related to the therapy and/or the cancer). As a set ofnon-limiting examples, biomarkers for use with the methods and systemsdescribed herein may include any group or subset of biomarkers listedherein, including those listed in the Tables (e.g., in Table 2). Such agroup or subset of biomarkers may include at least 3, at least 4, atleast 5, at least 6, at least 7, at least 8, at least 9, at least 10, atleast 20, at least 30, at least 40, at least 50, at least 60, at least70, at least 80, at least 90, at least 100, at least 200, at least 300,at least 400, at least 500, at least 600, at least 700, at least 800, atleast 900, or at least 1000 biomarkers. Such a group or subset ofbiomarkers may include up to 3, up to 4, up to 5, up to 6, up to 7, upto 8, up to 9, up to 10, up to 20, up to 30, up to 40, up to 50, up to60, up to 70, up to 80, up to 90, up to 100, up to 200, up to 300, up to400, up to 500, up to 600, up to 700, up to 800, up to 900, or up to1000 biomarkers.

A biomarker as described herein, in some embodiments, may be associatedwith multiple therapies. In some embodiments, a biomarker may beassociated with at least 2, at least 3, at least 4, at least 5, at least6, at least 7, at least 8, at least 9, at least 10, at least 20, atleast 30, at least 40, at least 50, at least 60, at least 70, at least80, at least 90, or at least 100 different therapies. In someembodiments, a biomarker may be associated with up to 2, up to 3, up to4, up to 5, up to 6, up to 7, up to 8, up to 9, up to 10, up to 20, upto 30, up to 40, up to 50, up to 60, up to 70, up to 80, up to 90, or upto 100 different therapies.

A biomarker as described herein, in some embodiments, may be associatedwith multiple cancers. In some embodiments, a biomarker may beassociated with at least 2, at least 3, at least 4, at least 5, at least6, at least 7, at least 8, at least 9, at least 10, at least 20, atleast 30, at least 40, at least 50, at least 60, at least 70, at least80, at least 90, or at least 100 different cancers. In some embodiments,a biomarker may be associated with up to 2, up to 3, up to 4, up to 5,up to 6, up to 7, up to 8, up to 9, up to 10, up to 20, up to 30, up to40, up to 50, up to 60, up to 70, up to 80, up to 90, or up to 100different cancers.

Biomarkers as provided herein may be associated with any biomolecule.Examples of a biomolecule include, but are not limited to, a growthfactor, a hormone, a steroid, a saccharide, a lipid, a heterocycliccompound, an elementary compound (e.g., iron), a metabolite, a vitamin,a neurotransmitter, and fatty acids. Such biomarkers may be referred toby the biomolecule that they are associated with. For example, abiomarker associated with a saccharide may be referred to as asaccharide biomarker; a biomarker associated with a lipid may bereferred to as a lipid biomarker; a biomarker associated with aheterocyclic compound may be referred to as a heterocyclic biomarker,and a biomarker associated with an elementary compound may be referredto as an elementary compound biomarker.

A “genetic biomarker,” as used herein, is a biomarker associated with agene or any product thereof (e.g., RNA, protein). Examples of a geneticbiomarker include, but are not limited to, a gene expression level(e.g., an increased expression level or a decreased expression level), agene mutation, a gene insertion, a gene deletion, a gene fusion, asingle nucleotide polymorphism (SNPs), and a gene copy number variation(CNV).

A genetic biomarker as described herein may be associated with any gene.In some embodiments, genes are group by a related function and/or otherproperty. Examples of gene groups include, but are not limited to, thefibroblasts group, the angiogenesis group, the tumor properties group,the anti-tumor immune microenvironment group, the tumor-promoting immunemicroenvironment group, the cancer associated fibroblasts group, theproliferation rate group, the PI3K/AKT/mTOR signaling group, theRAS/RAF/MEK signaling group, the receptor tyrosine kinases expressiongroup, the tumor suppressors group, the metastasis signature group, theanti-metastatic factors group, the mutation status group, the antigenpresentation group, the cytotoxic T and NK cells group, the B cellsgroup, the anti-tumor microenvironment group, the checkpoint inhibitiongroup, the Treg group, the MDSC group, the granulocytes group, thetumor-promotive immune group, the receptor tyrosine kinases expressiongroup, the growth factors group, the tumor suppressors group, themetastasis signature group, the anti-metastatic factors group, themutation status group, the MHCI group, the MHCII group, the coactivationmolecules group, the effector cells group, the NK cells group, the Tcell traffic group, the T cells group, the M1 signatures group, the Th1signature group, the antitumor cytokines group, the checkpointinhibition group, the M2 signature group, the Th2 signature group, theprotumor cytokines group, and the complement inhibition group.

In some embodiments, a genetic biomarker may be associated with some(e.g., at least three) genes from one or more of the following groups:the fibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1,TGFB2, TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3;the angiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1,PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1,MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; the tumorproperties group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1, AURKA, AURKB,CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2, MCM6, PIK3CA,PIK3CB, PIK3CG, PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, AKT3, BRAF, FNTA,FNTB, MAP2K1, MAP2K2, MKNK1, MKNK2, ALK, AXL, KIT, EGFR, ERBB2, FLT3,MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA, PDGFRB,NGF, CSF3, CSF2, FGF7, IGF1, IGF2, IL7, FGF2, TP53, SIK1, PTEN, DCN,MTAP, AIM2, RB1, ESRP1, CTSL, HOXA1, SMARCA4, SNAI2, TWIST1, NEDD9,PAPPA, HPSE, KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, MITF,APC, ARID1A, ATM, ATRX, BAP1, BRAF, BRCA2, CDH1, CDKN2A, CTCF, CTNNB1,DNMT3A, EGFR, FBXW7, FLT3, GATA3, HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3,NCOR1, NF1, NOTCH1, NPM1, NRAS, PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1,SETD2, STAG2, TAF1, TP53, and VHL; the anti-tumor immunemicroenvironment group: HLA-A, HLA-B, HLA-C, B2M, TAP1, TAP2, HLA-DRA,HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1, HLA-DPB1,HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, HLA-DRB6,CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, CD28, IFNG, GZMA, GZMB, PRF1,LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160,CD244, NCR1, KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1,KIR2DS2, KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7,CXCL11, CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E,CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, TRAT1, CD19, MS4A1, TNFRSF13C,CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A, CD79B, BLK, NOS2,IL12A, IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27,TBX21, LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG;the tumor-promoting immune microenvironment group: PDCD1, CD274, CTLA4,LAG3, PDCD1LG2, BTLA, HAVCR2, VSIR, CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, CCL28, IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2, TGFB3, NOS2, CYBB,CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2, CCL3, CCL5, CSF1,CXCL8, CXCL8, CXCL2, CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3,CCL26, PRG2, EPX, RNASE2, RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1,TPSAB1, MS4A2, CPA3, IL4, IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, CTSG,IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1,CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10, TGFB1, TGFB2,TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; the cancer associatedfibroblasts group: LGALS1, COL1A1, COL1A2, COL4A1, COL5A1, TGFB1, TGFB2,TGFB3, ACTA2, FGF2, FAP, LRP1, CD248, COL6A1, COL6A2, and COL6A3; theangiogenesis group: VEGFA, VEGFB, VEGFC, PDGFC, CXCL8, CXCR2, FLT1,PIGF, CXCL5, KDR, ANGPT1, ANGPT2, TEK, VWF, CDH5, NOS3, KDR, VCAM1,MMRN1, LDHA, HIF1A, EPAS1, CA9, SPP1, LOX, SLC2A1, and LAMP3; theproliferation rate group: MKI67, ESCO2, CETN3, CDK2, CCND1, CCNE1,AURKA, AURKB, CDK4, CDK6, PRC1, E2F1, MYBL2, BUB1, PLK1, CCNB1, MCM2,and MCM6; the PI3K/AKT/mTOR signaling group: PIK3CA, PIK3CB, PIK3CG,PIK3CD, AKT1, MTOR, PTEN, PRKCA, AKT2, and AKT3; the RAS/RAF/MEKsignaling group: BRAF, FNTA, FNTB, MAP2K1, MAP2K2, MKNK1, and MKNK2; thereceptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2,FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA,and PDGFRB; the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP,AIM2, and RB1; the metastasis signature group: ESRP1, CTSL, HOXA1,SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; the anti-metastaticfactors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, andMITF; the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF,BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3,HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS,PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, andVHL; the antigen presentation group: HLA-A, HLA-B, HLA-C, B2M, TAP1,TAP2, HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1,HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2,HLA-DRB6, CD80, CD86, CD40, CD83, TNFRSF4, ICOSLG, and CD28; thecytotoxic T and NK cells group: IFNG, GZMA, GZMB, PRF1, LCK, GZMK,ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, CD8B, NKG7, CD160, CD244, NCR1,KLRC2, KLRK1, CD226, GZMH, GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2,KIR2DS3, KIR2DS4, KIR2DS5, CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11,CCL21, CCL2, CCL3, CCL4, CCL5, EOMES, TBX21, ITK, CD3D, CD3E, CD3G,TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the B cells group: CD19,MS4A1, TNFRSF13C, CD27, CD24, CR2, TNFRSF17, TNFRSF13B, CD22, CD79A,CD79B, and BLK; the anti-tumor microenvironment group: NOS2, IL12A,IL12B, IL23A, TNF, IL1B, SOCS3, IFNG, IL2, CD40LG, IL15, CD27, TBX21,LTA, IL21, HMGB1, TNF, IFNB1, IFNA2, CCL3, TNFSF10, and FASLG; thecheckpoint inhibition group: PDCD1, CD274, CTLA4, LAG3, PDCD1LG2, BTLA,HAVCR2, and VSIR; the Treg group: CXCL12, TGFB1, TGFB2, TGFB3, FOXP3,CTLA4, IL10, TNFRSF1B, CCL17, CXCR4, CCR4, CCL22, CCL1, CCL2, CCL5,CXCL13, and CCL28; the MDSC group: IDO1, ARG1, IL4R, IL10, TGFB1, TGFB2,TGFB3, NOS2, CYBB, CXCR4, CD33, CXCL1, CXCL5, CCL2, CCL4, CCL8, CCR2,CCL3, CCL5, CSF1, and CXCL8; the granulocytes group: CXCL8, CXCL2,CXCL1, CCL11, CCL24, KITLG, CCL5, CXCL5, CCR3, CCL26, PRG2, EPX, RNASE2,RNASE3, IL5RA, GATA1, SIGLEC8, PRG3, CMA1, TPSAB1, MS4A2, CPA3, IL4,IL5, IL13, SIGLEC8, MPO, ELANE, PRTN3, and CTSG; the tumor-promotiveimmune group: IL10, VEGFA, TGFB1, IDO1, PTGES, MRC1, CSF1, LRP1, ARG1,PTGS1, MSR1, CD163, CSF1R, IL4, IL5, IL13, IL10, IL25, GATA3, IL10,TGFB1, TGFB2, TGFB3, IL22, MIF, CFD, CFI, CD55, CD46, and CR1; thereceptor tyrosine kinases expression group: ALK, AXL, KIT, EGFR, ERBB2,FLT3, MET, NTRK1, FGFR1, FGFR2, FGFR3, ERBB4, ERBB3, BCR-ABL, PDGFRA,and PDGFRB; the growth factors group: NGF, CSF3, CSF2, FGF7, IGF1, IGF2,IL7, and FGF2; the tumor suppressors group: TP53, SIK1, PTEN, DCN, MTAP,AIM2, and RB1; the metastasis signature group: ESRP1, CTSL, HOXA1,SMARCA4, SNAI2, TWIST1, NEDD9, PAPPA, and HPSE; the anti-metastaticfactors group: KISS1, ADGRG1, BRMS1, TCF21, CDH1, PCDH10, NCAM1, andMITF; the mutation status group: APC, ARID1A, ATM, ATRX, BAP1, BRAF,BRCA2, CDH1, CDKN2A, CTCF, CTNNB1, DNMT3A, EGFR, FBXW7, FLT3, GATA3,HRAS, IDH1, KRAS, MAP3K1, MTOR, NAV3, NCOR1, NF1, NOTCH1, NPM1, NRAS,PBRM1, PIK3CA, PIK3R1, PTEN, RB1, RUNX1, SETD2, STAG2, TAF1, TP53, andVHL; the MHCI group: HLA-A, HLA-B, HLA-C, B2M, TAP1, and TAP2; the MHCIIgroup: HLA-DRA, HLA-DRB1, HLA-DOB, HLA-DPB2, HLA-DMA, HLA-DOA, HLA-DPA1,HLA-DPB1, HLA-DMB, HLA-DQB1, HLA-DQA1, HLA-DRB5, HLA-DQA2, HLA-DQB2, andHLA-DRB6; the coactivation molecules group: CD80, CD86, CD40, CD83,TNFRSF4, ICOSLG, and CD28; the effector cells group: IFNG, GZMA, GZMB,PRF1, LCK, GZMK, ZAP70, GNLY, FASLG, TBX21, EOMES, CD8A, and CD8B; theNK cells group: NKG7, CD160, CD244, NCR1, KLRC2, KLRK1, CD226, GZMH,GNLY, IFNG, KIR2DL4, KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS4, and KIR2DS5;the T cell traffic group: CXCL9, CXCL10, CXCR3, CX3CL1, CCR7, CXCL11,CCL21, CCL2, CCL3, CCL4, and CCL5; the T cells group: EOMES, TBX21, ITK,CD3D, CD3E, CD3G, TRAC, TRBC1, TRBC2, LCK, UBASH3A, and TRAT1; the M1signatures group: NOS2, IL12A, IL12B, IL23A, TNF, IL1B, and SOCS3; theTh1 signature group: IFNG, IL2, CD40LG, IL15, CD27, TBX21, LTA, andIL21; the antitumor cytokines group: HMGB1, TNF, IFNB1, IFNA2, CCL3,TNFSF10, and FASLG; the M2 signature group: IL10, VEGFA, TGFB1, IDO1,PTGES, MRC1, CSF1, LRP1, ARG1, PTGS1, MSR1, CD163, and CSF1R; the Th2signature group: IL4, IL5, IL13, IL10, IL25, and GATA3; the protumorcytokines group: IL10, TGFB1, TGFB2, TGFB3, IL22, and MIF; and thecomplement inhibition group: CFD, CFI, CD55, CD46, and CR1.

A “protein biomarker,” as used herein, is a biomarker associated with aprotein. Examples of a protein biomarker include, but are not limitedto, a protein expression level (e.g., an increased expression level or adecreased expression level), a protein activity level (e.g., anincreased activity level or a decreased activity level), a proteinmutation, and a protein truncation.

A protein biomarker as described herein may associated with any protein.Examples of proteins related to protein biomarkers include, but are notlimited to, interferons, cytotoxic proteins, enzymes, cell adhesionproteins, extracellular matrix proteins, transcription factor proteins,intracellular signaling proteins, cytokines, chemokines, chemokinereceptors, and interleukins. Such biomarkers may be referred to by thebiomolecule for which they are related to, for example, interferonbiomarker, cytotoxic protein biomarker, enzyme biomarker, cell adhesionprotein biomarker, extracellular matrix protein biomarker, transcriptionfactor protein biomarker, intracellular signaling protein biomarker,cytokine biomarker, chemokine biomarker, chemokine receptor biomarker,and interleukin biomarker. Such protein biomarkers may include productsof, for example, any of the genes listed or referred to herein.

A “cellular biomarker,” as used herein, is a biomarker associated with acell. Examples of cellular biomarkers include, but are not limited to,numbers of types of one or more cells, percentage of one or more typesof cells, location of one or more cells, and structure or morphology ofone or more cells.

A cellular biomarker as described herein may be associated with anycell. Examples of cells include, but are not limited to, malignantcancer cells, leukocytes, lymphocytes, stromal cells, vascularendothelial cells, vascular pericytes, and myeloid-derived suppressorcells (MDSCs).

An “expression biomarker,” as used herein, is a biomarker associatedwith an expression of a gene or a product thereof (e.g., RNA, protein).Examples of expression biomarkers include, but are not limited to, anincreased expression level of a gene or product thereof, a decreasedexpression level of a gene or product thereof, expression of a truncatedgene or product thereof, and expression of a mutated gene or productthereof.

By comparing the expression level of a biomarker in a sample obtainedfrom a subject to a reference (or control), it can be determined whetherthe subject has an altered expression level (e.g., increased ordecreased) as compared to the reference (or control). For example, ifthe level of a biomarker in a sample from a subject deviates (e.g., isincreased or decreased) from the reference value (by e.g., 1%, 5%, 10%,20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 300%, 400%,500% or more from a reference value), the biomarker might be identifiedas an expression biomarker.

An “imaging biomarker,” as used herein, is a biomarker associated withimaging data. Examples of imaging biomarkers include, but are notlimited to, expression levels obtained from imaging data, numbers oftypes of one or more cells obtained from imaging data, and cancerlocation and/or progression obtained from imaging data.

An imaging biomarker as described herein may be associated with anyimaging data. Examples of imaging data include, but are not limited to,histological imaging data, immunohistological imaging data, magneticresonance imaging (MRI) data, ultrasound data, and x-ray data.

A “disease-state biomarker,” as used herein, is a biomarker associatedwith a state of a disease (e.g., cancer). Examples of disease-statebiomarkers include, but are not limited to, metastasis status (e.g.,absence or presence of metastasis), remission status (e.g., number ofprevious remissions, current remission), disease progression (e.g., low,moderate, or high disease progression), and cancer stage (e.g., stage 1,stage 2, stage 3, or stage 4).

Biomarkers as used herein encompasses any patient specific informationthat may be used to predict that patient's response to a therapy. Forexample, a personal habit of a patient (e.g., smoking) may be used as abiomarker to predict whether the patient is a responder or non-responderto a therapy.

A “personal habit biomarker,” as used herein, is a biomarker associatedwith a personal habit of a subject. Examples of personal habitbiomarkers include, but are not limited to, smoking (e.g., status as asmoker or non-smoker), frequency of exercise, alcohol use (e.g., low,moderate, high use of alcohol), and drug use (e.g., low, moderate, highuse of drugs).

In another example, a cultural or environmental factor experienced by apatient may play a role in whether the patient responds to a therapy.Such factors are used in systems and methods described herein to predicta patient's response to a therapy.

An “anthropological biomarker,” as used herein, is a biomarkerassociated with a culture and/or an environment of a subject. Examplesof anthropological biomarkers include, but are not limited to, stress(e.g., low, moderate, or high stress levels), economic status (e.g.,low, moderate, or high economic status), mental health (e.g., depressionor anxiety), and relationship status (e.g., married, single, divorced,or widowed).

From Biomarker Values to Normalized Biomarker Scores

Aspects of the present disclosure provide systems and methods thatnormalize biomarker scores to a common scale, thereby allowingcomparison of biomarker scores across different cell populations and/oramong different subjects.

Normalized biomarker scores may be determined for any number ofbiomarkers as described herein. As used herein, the term “normalizedbiomarker score” refers to a biomarker value that has been adjusted(e.g., normalized) to a common scale according to the techniquesdescribed herein.

In some embodiments, biomarker values are normalized to createnormalized biomarker scores based on a respective distribution of valuesfor each biomarker in a reference subset of biomarkers. In someembodiments, the reference subset of biomarkers comprises biomarkerinformation from any number of reference subjects. In one embodiment, a“reference subset” is a subset of biomarkers from one or more referencesubjects, the values of which may be used to normalize a biomarker of asubject.

As a non-limiting example, data may be available for up to 4,000biomarkers for a group of subjects. In this group of 4,000 biomarkers,1,000 biomarkers may be associated with a particular therapy (thuscreating a reference subset of 1,000 biomarkers). If, for a particularsubject being analyzed using the methods and systems described herein,values for 723 of these biomarkers are available (thus creating asubject subset of 723 biomarkers), a normalized biomarker score for eachof the 723 biomarkers may be computed using the distribution of valuesfor each particular biomarker. As another non-limiting example, in thisgroup of 4,000 biomarkers, 10 biomarkers may be associated with aparticular therapy (thus creating a reference subset of 10 biomarkers).If, for a particular subject being analyzed using the methods andsystems described herein, values for 7 of these biomarkers are available(thus creating a subject subset of 7 biomarkers), a normalized biomarkerscore for each of the 7 biomarkers may be computed using thedistribution of values for each particular biomarker.

In some embodiments, the reference subset of biomarkers comprisesbiomarker information from any number of subjects. In some embodiments,the reference subset of biomarkers comprises biomarker information fromat least 1, at least 2, at least 3, at least 4, at least 5, at least 6,at least 7, at least 8, at least 9, at least 10, at least 11, at least12, at least 13, at least 14, at least 15, at least 16, at least 17, atleast 18, at least 19, at least 20, at least 25, at least 30, at least35, at least 40, at least 45, at least 50, at least 55, at least 60, atleast 65, at least 70, at least 75, at least 80, at least 85, at least90, at least 95, at least 100, at least 200, at least 300, at least 400,at least 500, or at least 1000 subjects. In some embodiments, thereference subset of biomarkers comprises biomarker information from upto 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, upto 10, up to 11, up to 12, up to 13, up to 14, up to 15, up to 16, up to17, up to 18, up to 19, up to 20, up to 25, up to 30, up to 35, up to40, up to 45, up to 50, up to 55, up to 60, up to 65, up to 70, up to75, up to 80, up to 85, up to 90, up to 95, up to 100, up to 200, up to300, up to 400, up to 500, or up to 1000 subjects.

A reference subset of biomarkers may comprise any number of biomarkers.In some embodiments, the reference subset of biomarkers comprises atleast 1, at least 2, at least 3, at least 4, at least 5, at least 6, atleast 7, at least 8, at least 9, at least 10, at least 11, at least 12,at least 13, at least 14, at least 15, at least 16, at least 17, atleast 18, at least 19, at least 20, at least 25, at least 30, at least35, at least 40, at least 45, at least 50, at least 55, at least 60, atleast 65, at least 70, at least 75, at least 80, at least 85, at least90, at least 95, at least 100, at least 200, at least 300, at least 400,at least 500, or at least 1000 biomarkers. In some embodiments, thereference subset of biomarkers comprises up to 2, up to 3, up to 4, upto 5, up to 6, up to 7, up to 8, up to 9, up to 10, up to 11, up to 12,up to 13, up to 14, up to 15, up to 16, up to 17, up to 18, up to 19, upto 20, up to 25, up to 30, up to 35, up to 40, up to 45, up to 50, up to55, up to 60, up to 65, up to 70, up to 75, up to 80, up to 85, up to90, up to 95, up to 100, up to 200, up to 300, up to 400, up to 500, orup to 1000 biomarkers.

In some embodiments, biomarker values are normalized to createnormalized biomarker scores based on a respective distribution of valuesfor each biomarker in a subject subset of biomarkers. As used herein,the “subject subset” of biomarkers comprises biomarker information froma single subject. A subject subset of biomarkers may comprise any numberof biomarkers. In some embodiments, the subject subset of biomarkerscomprises at least 1, at least 2, at least 3, at least 4, at least 5, atleast 6, at least 7, at least 8, at least 9, at least 10, at least 11,at least 12, at least 13, at least 14, at least 15, at least 16, atleast 17, at least 18, at least 19, at least 20, at least 25, at least30, at least 35, at least 40, at least 45, at least 50, at least 55, atleast 60, at least 65, at least 70, at least 75, at least 80, at least85, at least 90, at least 95, at least 100, at least 200, at least 300,at least 400, at least 500, or at least 1000 biomarkers. In someembodiments, the subject subset of biomarkers comprises up to 2, up to3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, up to 10, up to11, up to 12, up to 13, up to 14, up to 15, up to 16, up to 17, up to18, up to 19, up to 20, up to 25, up to 30, up to 35, up to 40, up to45, up to 50, up to 55, up to 60, up to 65, up to 70, up to 75, up to80, up to 85, up to 90, up to 95, up to 100, up to 200, up to 300, up to400, up to 500, or up to 1000 biomarkers. In some embodiments, thesubject subset of biomarkers is identical to the reference subset ofbiomarkers (i.e., for a given calculation, system, or method describedherein).

Systems and methods described herein provide for determining any numberof normalized biomarker scores using sequencing data and biomarkerinformation. In some embodiments, systems and methods described hereinprovide for determining at least 1, at least 2, at least 3, at least 4,at least 5, at least 6, at least 7, at least 8, at least 9, at least 10,at least 11, at least 12, at least 13, at least 14, at least 15, atleast 16, at least 17, at least 18, at least 19, at least 20, at least25, at least 30, at least 35, at least 40, at least 45, at least 50, atleast 55, at least 60, at least 65, at least 70, at least 75, at least80, at least 85, at least 90, at least 95, at least 100, at least 200,at least 300, at least 400, at least 500, or at least 1000 normalizedbiomarker scores. In some embodiments, systems and methods describedherein provide for determining up to 2, up to 3, up to 4, up to 5, up to6, up to 7, up to 8, up to 9, up to 10, up to 11, up to 12, up to 13, upto 14, up to 15, up to 16, up to 17, up to 18, up to 19, up to 20, up to25, up to 30, up to 35, up to 40, up to 45, up to 50, up to 55, up to60, up to 65, up to 70, up to 75, up to 80, up to 85, up to 90, up to95, up to 100, up to 200, up to 300, up to 400, up to 500, or up to 1000normalized biomarker scores.

Systems and methods described herein, in some embodiments, provide fordetermining normalized biomarker scores for biomarkers associated with aparticular therapy. In some embodiments, systems and methods describedherein provide for determining normalized biomarker scores for at least1, at least 2, at least 3, at least 4, at least 5, at least 6, at least7, at least 8, at least 9, at least 10, at least 11, at least 12, atleast 13, at least 14, at least 15, at least 16, at least 17, at least18, at least 19, at least 20, at least 25, at least 30, at least 35, atleast 40, at least 45, at least 50, at least 55, at least 60, at least65, at least 70, at least 75, at least 80, at least 85, at least 90, atleast 95, at least 100, at least 200, at least 300, at least 400, atleast 500, or at least 1000 biomarkers associated with a particulartherapy. In some embodiments, systems and methods described hereinprovide for determining normalized biomarker scores for up to 1, up to2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, up to10, up to 11, up to 12, up to 13, up to 14, up to 15, up to 16, up to17, up to 18, up to 19, up to 20, up to 25, up to 30, up to 35, up to40, up to 45, up to 50, up to 55, up to 60, up to 65, up to 70, up to75, up to 80, up to 85, up to 90, up to 95, up to 100, up to 200, up to300, up to 400, up to 500, or up to 1000 biomarkers associated with aparticular therapy.

Systems and methods for normalization of biomarkers as described hereinmay be applied to biomarkers for any cancer (e.g., any tumor). Exemplarycancers include, but are not limited to, adrenocortical carcinoma,bladder urothelial carcinoma, breast invasive carcinoma, cervicalsquamous cell carcinoma, endocervical adenocarcinoma, colonadenocarcinoma, esophageal carcinoma, kidney renal clear cell carcinoma,kidney renal papillary cell carcinoma, liver hepatocellular carcinoma,lung adenocarcinoma, lung squamous cell carcinoma, ovarian serouscystadenocarcinoma, pancreatic adenocarcinoma, prostate adenocarcinoma,rectal adenocarcinoma, skin cutaneous melanoma, stomach adenocarcinoma,thyroid carcinoma, uterine corpus endometrial carcinoma, any type oflymphoma, leukemia, and cholangiocarcinoma.

Obtaining Biomarker Information

Biomarker information as described herein may be obtained from a varietyof sources. In some embodiments, biomarker information may be obtainedby analyzing a biological sample from a patient. The biological samplemay be analyzed prior to performance of the methods described herein forpredicting the efficacy of one or more treatments for the patient. Insome such embodiments, data obtained from the biological sample maystored (e.g., in a database) and accessed during performance of thetechniques described herein for predicting the efficacy of one or moretreatments for the patient. In some embodiments, biomarker informationis obtained from a database containing biomarker information for atleast one patient.

Biological Samples

Any biological sample from a subject (i.e., a patient or individual) maybe analyzed as described herein to obtain biomarker information. In someembodiments, the biological sample may be any sample from a subjectknown or suspected of having cancerous cells or pre-cancerous cells.

The biological sample may be from any source in the subject's bodyincluding, but not limited to, any fluid [such as blood (e.g., wholeblood, blood serum, or blood plasma), saliva, tears, synovial fluid,cerebrospinal fluid, pleural fluid, pericardial fluid, ascitic fluid,and/or urine], hair, skin (including portions of the epidermis, dermis,and/or hypodermis), oropharynx, laryngopharynx, esophagus, stomach,bronchus, salivary gland, tongue, oral cavity, nasal cavity, vaginalcavity, anal cavity, bone, bone marrow, brain, thymus, spleen, smallintestine, appendix, colon, rectum, anus, liver, biliary tract,pancreas, kidney, ureter, bladder, urethra, uterus, vagina, vulva,ovary, cervix, scrotum, penis, prostate, testicle, seminal vesicles,and/or any type of tissue (e.g., muscle tissue, epithelial tissue,connective tissue, or nervous tissue).

The biological sample may be any type of sample including, for example,a sample of a bodily fluid, one or more cells, a piece of tissue, orsome or all of an organ. In certain embodiments, one sample will betaken from a subject for analysis. In some embodiments, more than one(e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, or more) samples may be taken from a subject for analysis. In someembodiments, one sample from a subject will be analyzed. In certainembodiments, more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, or more) samples may be analyzed. Ifmore than one sample from a subject is analyzed, the samples may beprocured at the same time (e.g., more than one sample may be taken inthe same procedure), or the samples may be taken at different times(e.g., during a different procedure including a procedure 1, 2, 3, 4, 5,6, 7, 8, 9, 10 days; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 weeks; 1, 2, 3, 4, 5,6, 7, 8, 9, 10 months, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years, or 1, 2, 3,4, 5, 6, 7, 8, 9, 10 decades after a first procedure). A second orsubsequent sample may be taken or obtained from the same region (e.g.,from the same tumor or area of tissue) or a different region (including,e.g., a different tumor). A second or subsequent sample may be taken orobtained from the subject after one or more treatments, and may be takenfrom the same region or a different region. As a non-limiting example,the second or subsequent sample may be useful in determining whether thecancer in each sample has different characteristics (e.g., in the caseof samples taken from two physically separate tumors in a patient) orwhether the cancer has responded to one or more treatments (e.g., in thecase of two or more samples from the same tumor or different tumorsprior to and subsequent to a treatment).

Any of the biological samples described herein may be obtained from thesubject using any known technique. In some embodiments, the biologicalsample may be obtained from a surgical procedure (e.g., laparoscopicsurgery, microscopically controlled surgery, or endoscopy), bone marrowbiopsy, punch biopsy, endoscopic biopsy, or needle biopsy (e.g., afine-needle aspiration, core needle biopsy, vacuum-assisted biopsy, orimage-guided biopsy). In some embodiments, each of the at least onebiological samples is a bodily fluid sample, a cell sample, or a tissuebiopsy.

In some embodiments, one or more than one cell (i.e., a cell sample) maybe obtained from a subject using a scrape or brush method. The cellsample may be obtained from any area in or from the body of a subjectincluding, for example, from one or more of the following areas: thecervix, esophagus, stomach, bronchus, or oral cavity. In someembodiments, one or more than one piece of tissue (e.g., a tissuebiopsy) from a subject may be used. In certain embodiments, the tissuebiopsy may comprise one or more than one (e.g., 2, 3, 4, 5, 6, 7, 8, 9,10, or more than 10) samples from one or more tumors or tissues known orsuspected of having cancerous cells.

Sample Analysis

Systems and methods described herein are based, at least in part, on theidentification and characterization of certain biomarkers of a patientand/or the patient's cancer. Such information may be obtained from abiological sample of the subject (e.g., the patient) as describedherein.

Any type of analysis may be performed on a biological sample from asubject. In some embodiments, a blood analysis is performed on abiological sample from a subject. In some embodiments, a cytometryanalysis is performed on a biological sample from a subject. In someembodiments, a histological analysis is performed on a biological samplefrom a subject. In some embodiments, a immunohistological analysis isperformed on a biological sample from a subject.

Any type of sequencing data may be obtained from a biological sample ofa subject. In some embodiments, the sequencing data is DNA sequencingdata. In some embodiments, the sequencing data is RNA sequencing data.In some embodiments, the sequencing data is proteome sequencing data.

Such sequencing data may be obtained by any known technique. In someembodiments, the sequencing data is obtained from whole genomesequencing (WGS). In some embodiments, the sequencing data is obtainedfrom whole exome sequencing (WES). In some embodiments, the sequencingdata is obtained from whole transcriptome sequencing. In someembodiments, the sequencing data is obtained from mRNA sequencing. Insome embodiments, the sequencing data is obtained fromDNA/RNA-hybridization. In some embodiments, the sequencing data isobtained from microarray. In some embodiments, the sequencing data isobtained from DNA/RNA chip. In some embodiments, the sequencing data isobtained from PCR. In some embodiments, the sequencing data is obtainedfrom single nucleotide polymorphism (SNP) genotyping.

Expression data (e.g., indicating expression levels) for a plurality ofgenes may be obtained from a biological sample. There is no limit to thenumber of genes which may be examined. For example, there is no limit tothe number of genes for which the expression levels may be examined.

As a non-limiting example, four or more, five or more, six or more,seven or more, eight or more, nine or more, ten or more, eleven or more,twelve or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 ormore, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 ormore, 24 or more, 25 or more, 26 or more, 27 or more, 28 or more, 29 ormore, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 ormore, 90 or more, 100 or more, 125 or more, 150 or more, 175 or more,200 or more, 225 or more, 250 or more, 275 or more, or 300 or more genesmay be used for any evaluation described herein. As another set ofnon-limiting examples, at least four, at least five, at least six, atleast seven, at least eight, at least nine, at least ten, at leasteleven, at least twelve, at least 13, at least 14, at least 15, at least16, at least 17, at least 18, at least 19, at least 20, at least 21, atleast 22, at least 23, at least 24, at least 25, at least 26, at least27, at least 28, at least 29, at least 30, at least 40, at least 50, atleast 60, at least 70, at least 80, at least 90, at least 100, at least125, at least 150, at least 175, at least 200, at least 225, at least250, at least 275, or at least 300 genes may be used for any evaluationdescribed herein. As a further set of non-limiting examples, up to four,up to five, up to six, up to seven, up to eight, up to nine, up to ten,up to eleven, up to twelve, up to 13, up to 14, up to 15, up to 16, upto 17, up to 18, up to 19, up to 20, up to 21, up to 22, up to 23, up to24, up to 25, up to 26, up to 27, up to 28, up to 29, up to 30, up to40, up to 50, up to 60, up to 70, up to 80, up to 90, up to 100, up to125, up to 150, up to 175, up to 200, up to 225, up to 250, up to 275,or up to 300 genes may be used for any evaluation described herein.

Any method may be used on a sample from a subject in order to acquireexpression data (e.g., indicating expression levels) for the pluralityof genes. As a set of non-limiting examples, the expression data may beRNA expression data, DNA expression data, or protein expression data.

DNA expression data, in some embodiments, refers to a level of DNA in asample from a subject. The level of DNA in a sample from a subjecthaving cancer may be elevated compared to the level of DNA in a samplefrom a subject not having cancer, e.g., a gene duplication in a cancerpatient's sample. The level of DNA in a sample from a subject havingcancer may be reduced compared to the level of DNA in a sample from asubject not having cancer, e.g., a gene deletion in a cancer patient'ssample.

DNA expression data, in some embodiments, refers to data for DNA (orgene) expressed in a sample, for example, sequencing data for a genethat is expressed in a patient's sample. Such data may be useful, insome embodiments, to determine whether the patient has one or moremutations associated with a particular cancer.

RNA expression data may be acquired using any method known in the artincluding, but not limited to: whole transcriptome sequencing, total RNAsequencing, mRNA sequencing, targeted RNA sequencing, small RNAsequencing, ribosome profiling, RNA exome capture sequencing, and/ordeep RNA sequencing. DNA expression data may be acquired using anymethod known in the art including any known method of DNA sequencing.For example, DNA sequencing may be used to identify one or moremutations in the DNA of a subject. Any technique used in the art tosequence DNA may be used with the methods and systems described herein.As a set of non-limiting examples, the DNA may be sequenced throughsingle-molecule real-time sequencing, ion torrent sequencing,pyrosequencing, sequencing by synthesis, sequencing by ligation (SOLiDsequencing), nanopore sequencing, or Sanger sequencing (chaintermination sequencing). Protein expression data may be acquired usingany method known in the art including, but not limited to: N-terminalamino acid analysis, C-terminal amino acid analysis, Edman degradation(including though use of a machine such as a protein sequenator), ormass spectrometry.

In some embodiments, the expression data comprises whole exomesequencing (WES) data. In some embodiments, the expression datacomprises whole genome sequencing (WGS) data. In some embodiments, theexpression data comprises next-generation sequencing (NGS) data. In someembodiments, the expression data comprises microarray data.

Datasets

Any dataset containing information associated with a biomarker may beused to obtain biomarker information as described herein. In someembodiments, biomarker informationmay be obtained from one or moredatabases and/or any other suitable electronic repository of data.Examples of databases include, but are not limited to, CGP (CancerGenome Project), CPTAC (Clinical Proteomic Tumor Analysis Consortium),ICGC (International Cancer Genome Consortium), and TCGA (The CancerGenome Atlas). In some embodiments, biomarker information may beobtained from data associated with a clinical trial. In someembodiments, biomarker information may be predicted in association witha clinical trial based on one or more similar drugs (e.g., drugs of asimilar class such as PD-1 inhibitors). In some embodiments, biomarkerinformation may be obtained from a hospital database. In someembodiments, biomarker information may be obtained from a commercialsequencing supplier. In some embodiments, biomarker information may beobtained from a subject (e.g., a patient) and/or a subject's (e.g., apatient's) relative, guardian, or caretaker.

Assays

Any of the biological samples described herein can be used for obtainingexpression data using conventional assays or those described herein.Expression data, in some embodiments, includes gene expression levels.Gene expression levels may be detected by detecting a product of geneexpression such as mRNA and/or protein.

In some embodiments, gene expression levels are determined by detectinga level of a protein in a sample and/or by detecting a level of activityof a protein in a sample. As used herein, the terms “determining” or“detecting” may include assessing the presence, absence, quantity and/oramount (which can be an effective amount) of a substance within asample, including the derivation of qualitative or quantitativeconcentration levels of such substances, or otherwise evaluating thevalues and/or categorization of such substances in a sample from asubject.

The level of a protein may be measured using an immunoassay. Examples ofimmunoassays include any known assay (without limitation), and mayinclude any of the following: immunoblotting assay (e.g., Western blot),immunohistochemical analysis, flow cytometry assay, immunofluorescenceassay (IF), enzyme linked immunosorbent assays (ELISAs) (e.g., sandwichELISAs), radioimmunoassays, electrochemiluminescence-based detectionassays, magnetic immunoassays, lateral flow assays, and relatedtechniques. Additional suitable immunoassays for detecting a level of aprotein provided herein will be apparent to those of skill in the art.

Such immunoassays may involve the use of an agent (e.g., an antibody)specific to the target protein. An agent such as an antibody that“specifically binds” to a target protein is a term well understood inthe art, and methods to determine such specific binding are also wellknown in the art. An antibody is said to exhibit “specific binding” ifit reacts or associates more frequently, more rapidly, with greaterduration and/or with greater affinity with a particular target proteinthan it does with alternative proteins. It is also understood by readingthis definition that, for example, an antibody that specifically bindsto a first target peptide may or may not specifically or preferentiallybind to a second target peptide. As such, “specific binding” or“preferential binding” does not necessarily require (although it caninclude) exclusive binding. Generally, but not necessarily, reference tobinding means preferential binding. In some examples, an antibody that“specifically binds” to a target peptide or an epitope thereof may notbind to other peptides or other epitopes in the same antigen. In someembodiments, a sample may be contacted, simultaneously or sequentially,with more than one binding agent that binds different proteins (e.g.,multiplexed analysis).

As used herein, the term “antibody” refers to a protein that includes atleast one immunoglobulin variable domain or immunoglobulin variabledomain sequence. For example, an antibody can include a heavy (H) chainvariable region (abbreviated herein as VH), and a light (L) chainvariable region (abbreviated herein as VL). In another example, anantibody includes two heavy (H) chain variable regions and two light (L)chain variable regions. The term “antibody” encompasses antigen-bindingfragments of antibodies (e.g., single chain antibodies, Fab and sFabfragments, F(ab′)2, Fd fragments, Fv fragments, scFv, and domainantibodies (dAb) fragments (de Wildt et al., Eur J Immunol. 1996;26(3):629-39.)) as well as complete antibodies. An antibody can have thestructural features of IgA, IgG, IgE, IgD, IgM (as well as subtypesthereof). Antibodies may be from any source including, but not limitedto, primate (human and non-human primate) and primatized (such ashumanized) antibodies.

In some embodiments, the antibodies as described herein can beconjugated to a detectable label and the binding of the detectionreagent to the peptide of interest can be determined based on theintensity of the signal released from the detectable label.Alternatively, a secondary antibody specific to the detection reagentcan be used. One or more antibodies may be coupled to a detectablelabel. Any suitable label known in the art can be used in the assaymethods described herein. In some embodiments, a detectable labelcomprises a fluorophore. As used herein, the term “fluorophore” (alsoreferred to as “fluorescent label” or “fluorescent dye”) refers tomoieties that absorb light energy at a defined excitation wavelength andemit light energy at a different wavelength. In some embodiments, adetection moiety is or comprises an enzyme. In some embodiments, anenzyme is one (e.g., β-galactosidase) that produces a colored productfrom a colorless substrate.

It will be apparent to those of skill in the art that this disclosure isnot limited to immunoassays. Detection assays that are not based on anantibody, such as mass spectrometry, are also useful for the detectionand/or quantification of a protein and/or a level of protein as providedherein. Assays that rely on a chromogenic substrate can also be usefulfor the detection and/or quantification of a protein and/or a level ofprotein as provided herein.

Alternatively, the level of nucleic acids encoding a gene in a samplecan be measured via a conventional method. In some embodiments,measuring the expression level of nucleic acid encoding the genecomprises measuring mRNA. In some embodiments, the expression level ofmRNA encoding a gene can be measured using real-time reversetranscriptase (RT) Q-PCR or a nucleic acid microarray. Methods to detectnucleic acid sequences include, but are not limited to, polymerase chainreaction (PCR), reverse transcriptase-PCR (RT-PCR), in situ PCR,quantitative PCR (Q-PCR), real-time quantitative PCR (RT Q-PCR), in situhybridization, Southern blot, Northern blot, sequence analysis,microarray analysis, detection of a reporter gene, or other DNA/RNAhybridization platforms.

In some embodiments, the level of nucleic acids encoding a gene in asample can be measured via a hybridization assay. In some embodiments,the hybridization assay comprises at least one binding partner. In someembodiments, the hybridization assay comprises at least oneoligonucleotide binding partner. In some embodiments, the hybridizationassay comprises at least one labeled oligonucleotide binding partner. Insome embodiments, the hybridization assay comprises at least one pair ofoligonucleotide binding partners. In some embodiments, the hybridizationassay comprises at least one pair of labeled oligonucleotide bindingpartners.

Any binding agent that specifically binds to a desired nucleic acid orprotein may be used in the methods and kits described herein to measurean expression level in a sample. In some embodiments, the binding agentis an antibody or an aptamer that specifically binds to a desiredprotein. In other embodiments, the binding agent may be one or moreoligonucleotides complementary to a nucleic acid or a portion thereof.In some embodiments, a sample may be contacted, simultaneously orsequentially, with more than one binding agent that binds differentproteins or different nucleic acids (e.g., multiplexed analysis).

To measure an expression level of a protein or nucleic acid, a samplecan be in contact with a binding agent under suitable conditions. Ingeneral, the term “contact” refers to an exposure of the binding agentwith the sample or cells collected therefrom for suitable periodsufficient for the formation of complexes between the binding agent andthe target protein or target nucleic acid in the sample, if any. In someembodiments, the contacting is performed by capillary action in which asample is moved across a surface of the support membrane.

In some embodiments, an assay may be performed in a low-throughputplatform, including single assay format. In some embodiments, an assaymay be performed in a high-throughput platform. Such high-throughputassays may comprise using a binding agent immobilized to a solid support(e.g., one or more chips). Methods for immobilizing a binding agent willdepend on factors such as the nature of the binding agent and thematerial of the solid support and may require particular buffers. Suchmethods will be evident to one of ordinary skill in the art.

Genes

The various genes recited herein are, in general, named using human genenaming conventions. The various genes, in some embodiments, aredescribed in publically available resources such as published journalarticles. The gene names may be correlated with additional information(including sequence information) through use of, for example, the NCBIGenBank® databases available at www <dot> ncbi <dot> nlm <dot> nih <dot>gov; the HUGO (Human Genome Organization) Gene Nomination Committee(HGNC) databases available at www <dot> genenames <dot> org; the DAVIDBioinformatics Resource available at www <dot> david <dot> ncifcrf <dot>gov. It should be appreciated that a gene may encompass all variants ofthat gene. For organisms or subjects other than human subjects,corresponding specific-specific genes may be used. Synonyms,equivalents, and closely related genes (including genes from otherorganisms) may be identified using similar databases including the NCBIGenBank® databases described above.

In some embodiments, gene AXL may be identified as GenBank® Accessionnumber NM_199054.2 or NM_017572.3.; gene CCL2 may be identified asGenBank® Accession number NM_002982.3; gene CCL7 may be identified asGenBank® Accession number NM_006273.3; gene CCL8 may be identified asGenBank® Accession number NM_005623.2; gene CDH1 may be identified asGenBank® Accession number NM_004360.4, NM_001317184.1, NM_001317185.1,or NM_001317186.1; gene VEGFC may be identified as GenBank® Accessionnumber NM_005429.4; gene EGFR may be identified as GenBank® Accessionnumber NM_001346941.1, NM_005228.4, NM_001346898.1, NM_001346900.1,NM_001346899.1, NM_001346897.1, NM_201284.1, NM_201283.1 or NM_201282.1;gene ROR2 may be identified as GenBank® Accession number NM_004560.3 orNM_001318204.1; gene PTEN may be identified as GenBank® Accession numberNM_001304717.2, NM_000314.6 or NM_001304718.1; gene TAGLN may beidentified as GenBank® Accession number NM_001001522.2 or NM_003186.4.

Predicting Therapy Response

Normalized biomarker scores derived from a patient and/or a patient'sbiological sample as described herein may be used for various clinicalpurposes including, for example, identifying subjects suitable for aparticular treatment (e.g., an immunotherapy), and/or predictinglikelihood of a patient's response or lack thereof to a particulartreatment. Accordingly, described herein are prognostic methods forpredicting therapy efficacy, for example, an immunotherapy, based on apatient's biomarker values. Additionally, the systems and methodsdescribed herein may be used to predict whether a patient (subject) mayor may not have one or more adverse reactions to a particular therapy,based on the patient's biomarker values (e.g., whether a subject islikely to have immune-mediated adverse reactions to checkpoint blockadetherapy and/or not have immune-mediated adverse reactions to checkpointblockade therapy).

To practice methods for predicting therapeutic efficacy as describedherein, a therapy score for a patient may be determined for a particulartherapy. As used herein, the term “therapy score” is calculated usingmultiple normalized biomarker scores for a patient that is indicative ofa predicted response of that subject to a therapy. As a set ofnon-limiting examples, such a “therapy score” may be calculated usingmultiple normalized biomarker scores in one or more of the followingways: 1) as a sum; 2) as a weighted sum (e.g., in a regression model);3) using any linear or generalized linear model taking the normalizedbiomarker scores as inputs and producing, based on the input normalizedbiomarker scores, an output indicative of a patient's predicted responseto a therapy; 4) using any statistical model (e.g., a neural networkmodel, a Bayesian regression model, an adaptive non-linear regressionmodel, a support vector regression model, a Gaussian mixture model,random forest regression, and/or any other suitable type mixture model)taking the normalized biomarker scores as inputs and producing, based onthe input biomarker scores, an output indicative of a patient'spredicted response to a therapy.

A therapy score as described herein includes a therapy score calculatedusing any suitable number of normalized biomarker scores. In someembodiments, the therapy score may be calculated using at least 2normalized biomarker scores. In some embodiments, the therapy score maybe calculated using at least 3, at least 4, at least 5, at least 6, atleast 7, at least 8, at least 9, at least 10, at least 20, at least 30,at least 40, at least 50, at least 60, at least 70, at least 80, atleast 90, or at least 100 normalized biomarker scores.

In some embodiments, a therapy score is calculated using one or morenormalized biomarker values which may be weighted by one or morerespective weights as part of the calculation. A biomarker weight may beassigned to any biomarker. For example, an abundant biomarker may beassigned a higher weight for predicting a therapy response. Such weightsmay be determined, for example, using a machine learning technique. As anon-limiting set of examples, such weights may be determined by traininga regression model (e.g., a linear regression model, a generalizedlinear model, a support vector regression model, a logistic regressionmodel, a random forest regression model, a neural network model, etc.).

A therapy score for a therapy may be a positive value or a negativevalue. A positive therapy score, in some embodiments, is indicative of apositive response to a therapy. A negative therapy score, in someembodiments, is indicative of a negative response or no response to atherapy. A therapy score close to zero, in some embodiments, isindicative of little or no measurable response to a therapy.

A therapy score, in some embodiments, more accurately predicts apatient's response to a therapy when compared, for example, to using asingle biomarker. For example, a patient's response to a therapy may bemore accurately predicted as a therapy score positively increases innumeric value. In another example, a patient's lack of response to atherapy may be more accurately predicted as a therapy score negativelyincreases in numeric value.

The terms “subject” or “patient” may be used interchangeably and referto a subject who needs the analysis as described herein. In someembodiments, the subject is a human or a non-human mammal (e.g., anon-human primate). In some embodiments, the subject is suspected tohave cancer or is at risk for cancer. In some embodiments, the subjecthas (e.g., is known to have) cancer. Examples of cancer include, withoutlimitation, adrenocortical carcinoma, bladder urothelial carcinoma,breast invasive carcinoma, cervical squamous cell carcinoma,endocervical adenocarcinoma, colon adenocarcinoma, esophageal carcinoma,kidney renal clear cell carcinoma, kidney renal papillary cellcarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lungsquamous cell carcinoma, ovarian serous cystadenocarcinoma, pancreaticadenocarcinoma, prostate adenocarcinoma, rectal adenocarcinoma, skincutaneous melanoma, stomach adenocarcinoma, thyroid carcinoma, uterinecorpus endometrial carcinoma, one or more types of leukemia, andcholangiocarcinoma.

In some embodiments, the subject is a human patient having one or moresymptom of a cancer. For example, the subject may have fatigue, pain,weakness or numbness, loss of bladder or bowl control, cough,blood-tinged saliva, anemia, breast lump or discharge, or a combinationthereof. In some embodiments, the subject has a symptom of cancer or hasa history of a symptom of cancer. In some embodiments, the subject hasmore than one symptom of cancer or has a history of more than onesymptoms of cancer. In some embodiments, the subject has no symptom ofcancer, has no history of a symptom of cancer, or has no history ofcancer.

Such a subject may exhibit one or more symptoms associated with acancer. Alternatively or in addition, such a subject may have one ormore risk factors for cancer, for example, an environmental factorassociated with cancer (e.g., geographic location or exposure to amutagen), a family history of cancer, and/or a genetic predisposition todeveloping cancer.

Alternatively, the subject who needs the analysis described herein maybe a patient having cancer or suspected of having cancer. Such a subjectmay currently be having a relapse, or may have suffered from the diseasein the past (e.g., may be currently relapse-free), or may have cancer.In some examples, the subject is a human patient who may be on atreatment (i.e., the subject may be receiving treatment) for the diseaseincluding, for example, a treatment involving chemotherapy or radiationtherapy. In other instances, such a human patient may be free of such atreatment.

Impact Scores

In some embodiments, the systems and methods described herein may beused to assess the effectiveness of a therapy over time. In someembodiments, aspects of the disclosure provide methods and systems forusing normalized biomarker scores obtained from samples prior to andsubsequent to administration of a candidate therapy to determine theefficacy of that therapy. In some embodiments, such methods may also beused to select a candidate therapy for use with a patient or subject. Incertain embodiments, such methods may be used to assess the impact of acandidate therapy, which impact may be quantified by determining animpact score, in accordance with some embodiments described herein.

For example, some embodiments provide for determining, using a first andsecond set of normalized biomarker scores for a subject, an impact scorefor a candidate therapy, wherein the first and second set of normalizedbiomarker scores are determined using first sequencing data about atleast one biological sample of a subject prior to administration of thecandidate therapy, and second sequencing data about at least onebiological sample of a subject subsequent to administration of thecandidate therapy. Such an impact score would be indicative of response(e.g., a positive or negative response) of the subject to administrationof the candidate therapy.

Computer Implemented Methods for Predicting or Describing TherapyResponse

Aspects of the disclosure provide computer implemented methods fordetermining, using a set of normalized biomarker scores, biomarkerscores for a subject indicative of a patient's response or lack thereofto a particular therapy.

In some embodiments, a software program may provide a user with a visualrepresentation presenting information related to a patient's biomarkersscores (e.g., a biomarker score, and/or a therapy score, and/or animpact score), and predicted efficacy of a therapy. Such a softwareprogram may execute in any suitable computing environment including, butnot limited to, a cloud-computing environment, a device co-located witha user (e.g., the user's laptop, desktop, smartphone, etc.), one or moredevices remote from the user (e.g., one or more servers), etc.

For example, in some embodiments, the techniques described herein may beimplemented in the illustrative environment 100 shown in FIG. 1A. Asshown in FIG. 1A, within illustrative environment 100, one or morebiological samples of a patient 102 may be provided to a laboratory 104.Laboratory 104 may process the biological sample(s) to obtain sequencingdata (e.g., transcriptome, exome, and/or genome sequencing data) andprovide it, via network 108, to at least one database 106 that storesinformation about patient 102.

Network 108 may be a wide area network (e.g., the Internet), a localarea network (e.g., a corporate Intranet), and/or any other suitabletype of network. Any of the devices shown in FIG. 1A may connect to thenetwork 108 using one or more wired links, one or more wireless links,and/or any suitable combination thereof.

In the illustrated embodiment of FIG. 1A, the at least one database 106may store sequencing data for the patient, expression data for thepatient, medical history data for the patient, test result data for thepatient, and/or any other suitable information about the patient 102.Examples of stored test result data for the patient include biopsy testresults, imaging test results (e.g., MRI results), and blood testresults. The information stored in at least one database 106 may bestored in any suitable format and/or using any suitable datastructure(s), as aspects of the technology described herein are notlimited in this respect. The at least one database 106 may store data inany suitable way (e.g., one or more databases, one or more files). Theat least one database 106 may be a single database or multipledatabases.

As shown in FIG. 1A, illustrative environment 100 includes one or moreexternal databases 116, which may store information for patients otherthan patient 102. For example, external databases 116 may storeexpression data (of any suitable type) for one or more patients, medicalhistory data for one or more patients, test result data (e.g., imagingresults, biopsy results, blood test results) for one or more patients,demographic and/or biographic information for one or more patients,and/or any other suitable type of information. In some embodiments,external database(s) 116 may store information available in one or morepublically accessible databases such as TCGA (The Cancer Genome Atlas),one or more databases of clinical trial information, and/or one or moredatabases maintained by commercial sequencing suppliers. The externaldatabase(s) 116 may store such information in any suitable way using anysuitable hardware, as aspects of the technology described herein are notlimited in this respect.

In some embodiments, the at least one database 106 and the externaldatabase(s) 116 may be the same database, may be part of the samedatabase system, or may be physically co-located, as aspects of thetechnology described herein are not limited in this respect.

In some embodiments, information stored in patient information database106 and/or in external database(s) 116 may be used to perform any of thetechniques described herein related to determining a therapy scoreand/or impact score indicative of a patient's response to a therapy. Forexample, the information stored in the database(s) 106 and/or 116 may beaccessed, via network 108, by software executing on server(s) 110 toperform any one or more of the techniques described herein in connectionwith FIGS. 2A, 2B, 2C, 2D and 2E.

For example, in some embodiments, server(s) 110 may access informationstored in database(s) 106 and/or 116 and use this information to performprocess 200, described with reference to FIG. 2A, for determiningtherapy scores for multiple therapies based on normalized biomarkerscores.

As another example, server(s) 110 may access information stored indatabase(s) 106 and/or 116 and use this information to perform process220, described with reference to FIG. 2B, for determining theeffectiveness of a candidate therapy on a patient.

As another example, server(s) 110 may access information stored indatabase(s) 106 and/or 116 and use this information to perform process240, described with reference to FIG. 2C, for determining therapy scoresfor at least two selected therapies based on normalized biomarker scoresfor at least three biomarkers for each of the therapies.

As another example, server(s) 110 may access information stored indatabase(s) 106 and/or 116 and use this information to perform process260, described with reference to FIG. 2D, for obtaining first and secondtherapy scores for first and second therapies.

As yet another example, server(s) 110 may access information stored indatabase(s) 106 and/or 116 and use this information to perform process280, described with reference to FIG. 2E, for identifying a subject as amember of a cohort using normalized biomarker scores.

In some embodiments, server(s) 110 may include one or multiple computingdevices. When server(s) 110 include multiple computing devices, thedevice(s) may be physically co-located (e.g., in a single room) ordistributed across multi-physical locations. In some embodiments,server(s) 110 may be part of a cloud computing infrastructure. In someembodiments, one or more server(s) 110 may be co-located in a facilityoperated by an entity (e.g., a hospital, research institution) withwhich doctor 114 is affiliated. In such embodiments, it may be easier toallow server(s) 110 to access private medical data for the patient 102.

As shown in FIG. 1A, in some embodiments, the results of the analysisperformed by server(s) 110 may be provided to doctor 114 through acomputing device 114 (which may be a portable computing device, such asa laptop or smartphone, or a fixed computing device such as a desktopcomputer). The results may be provided in a written report, an e-mail, agraphical user interface, and/or any other suitable way. It should beappreciated that although in the embodiment of FIG. 1A, the results areprovided to a doctor, in other embodiments, the results of the analysismay be provided to patient 102 or a caretaker of patient 102, ahealthcare provider such as a nurse, or a person involved with aclinical trial.

In some embodiments, the results may be part of a graphical userinterface (GUI) presented to the doctor 114 via the computing device112. In some embodiments, the GUI may be presented to the user as partof a webpage displayed by a web browser executing on the computingdevice 112. In some embodiments, the GUI may be presented to the userusing an application program (different from a web-browser) executing onthe computing device 112. For example, in some embodiments, thecomputing device 112 may be a mobile device (e.g., a smartphone) and theGUI may be presented to the user via an application program (e.g., “anapp”) executing on the mobile device.

The GUI presented on computing device 112 provides a wide range ofoncological data relating to both the patient and the patient's cancerin a new way that is compact and highly informative. Previously,oncological data was obtained from multiple sources of data and atmultiple times making the process of obtaining such information costlyfrom both a time and financial perspective. Using the techniques andgraphical user interfaces illustrated herein, a user can access the sameamount of information at once with less demand on the user and with lessdemand on the computing resources needed to provide such information.Low demand on the user serves to reduce clinician errors associated withsearching various sources of information. Low demand on the computingresources serves to reduce processor power, network bandwidth, andmemory needed to provide a wide range of oncological data, which is animprovement in computing technology.

FIG. 1B shows a block diagram of an illustrative GUI 150 containinginformation about patient 102. GUI 150 may include separate portionsproviding different types of information about patient 102. IllustrativeGUI 150 includes the following portions: Patient Information Portion152, Molecular-Functional (MF) Portrait Portion 160, Clinical TrialInformation Portion 162, Immunotherapy Portion 154, Efficacy PredictorPortion 156, and Targeted Therapy Selection Portion 158.

Patient Information Portion 152 may provide general information aboutthe patient and the patient's cancer. General information about thepatient may include such information as the patient's name and date ofbirth, the patient's insurance provider, and contact information for thepatient such as address and phone number. General information about thepatient's cancer may include the patient's diagnosis, the patient'shistory of relapse and/or remission, and information relating to stageof the patient's cancer. Patient Information Portion 152 may alsoprovide information relating to potential treatment options for thepatient and/or previously administered treatments.

Molecular-Functional (MF) Portrait Portion 160 may include a molecularfunctional tumor portrait (MF profile) which refers to a graphicaldepiction of a tumor with regard to its molecular and cellularcomposition, and biological processes that are present within and/orsurrounding the tumor. Further aspects relating to a patient's MFprofile are provided in International patent application numberPCT/US18/37017, entitled “Systems and Methods for Generating,Visualizing and Classifying Molecular Functional Profiles,” filed Jun.12, 2018, the entire contents of which are incorporated herein byreference.

Clinical Trial Information Portion 162 may include information relatingto a clinical trial for a therapy that may be and/or will beadministered to the patient. Clinical Trial Information Portion 162 mayprovide information about an ongoing clinical trial or a completedclinical trial. Information that may be provided in Clinical TrialInformation Portion 162 may include information related to a therapyused in the clinical trial such as dosage and dosage regimen, number anddiagnosis of patients participating in the clinical trial, and patientoutcomes.

Immunotherapy Portion 154 may include patient specific information as itrelates to an immunotherapy. Immunotherapy Portion 154 may provide suchinformation for different immunotherapies, for example, immunecheckpoint blockade therapies, anti-cancer vaccine therapies, and T celltherapies. Patient specific information relating to an immunotherapy mayinclude information about the patient such as the patient's biomarkersassociated with an immunotherapy and/or information about the patient'scancer such as composition of immune cells in the patient's tumor.

Efficacy Predictor Portion 156 may include information indicative of thepatient's predicted response to an immunotherapy based on patientspecific information presented in Immunotherapy Portion 154. EfficacyPredictor Portion 156 may provide predicted efficacy of an immunotherapydetermined, in some embodiments, using a patient's biomarkers asdescribed in herein. Additionally or alternatively, Efficacy PredictorPortion 156 may provide predicted efficacy of an immune checkpointblockade therapy determined using patient specific information such asgene expression data as described in International patent applicationnumber PCT/US18/37018, entitled “Systems and Methods for IdentifyingResponders and Non-Responders to Immune Checkpoint Blockade Therapy,”filed Jun. 12, 2018, the entire contents of which are incorporatedherein by reference.

Targeted Therapy Selection Portion 158 may include patient specificinformation as it relates to a targeted therapy. Targeted TherapySelection Portion 158 may provide such information for differenttargeted therapies, for example, a kinase inhibitor therapy, achemotherapy, and anti-cancer antibody therapy. Patient specificinformation relating to an a targeted therapy may include informationabout the patient such as the patient's biomarkers associated with atargeted therapy and/or information about the patient's cancer such aswhether a mutation is present in the patient's tumor.

An illustrative example of the graphical user interface 150 of FIG. 1Bis shown as graphical user interface 170 of FIG. 1C. As shown in FIG.1C, Patient Information Portion 172 may provide different information indifferent panels, for example, Overall Status panel, DiseaseCharacteristics panel, and General Recommendations panel. Overall Statuspanel, in some embodiments, may provide general information about thepatient such as patient name and patient age. Disease Characteristicspanel, in some embodiments, may provide information about the patient'scancer such as type of cancer and stage of cancer. GeneralRecommendations panel, in some embodiments, may provide previoustreatments and possible treatment options for the patient.

Clinical Trial Information Portion 182 a provides information relatingto a clinical trial for anti-PD1 therapy. Clinical Trial InformationPortion 182 a (as shown in the upper portion) shows a graph providingpatient overall response rate (ORR) for anti-PD1 therapy and othertherapies such as vaccine or IFNα therapies. A user may select portionsof the Clinical Trial Information Portion 182 a to access informationrelated to patient progression-free survival (PFS) and/or patientoverall survival (OS). Clinical Trial Information Portion 182 a (asshown in the lower portion) provides information relating to differentclinical trials that may be presented to a user including a briefdescription of the clinical trial.

Clinical Trial Information Portion 182 b provides information relatingto a clinical trial for different targeted therapies. Clinical TrialInformation Portion 182 b (as shown in the upper portion) shows a graphproviding patient overall response rate (ORR) for different targetedtherapies including sunitinib (SU), imatinib (IM), vemurafenib (VER) anddabrafenib (DAB). A user may select portions of the Clinical TrialInformation Portion 182 b to access information related to patientprogression-free survival (PFS) and/or patient overall survival (OS).Clinical Trial Information Portion 182 b (as shown in the lower portion)provides information relating to different clinical trials that may bepresented to a user including a brief description of the clinical trial.

Immunotherapy Portion 174 provides patient specific informationassociated with an immunotherapy and information indicative of thepatient's predicted response to that immunotherapy. ImmunotherapyPortion 174 provides such information for anti-PD1 therapy, atherapeutic cancer vaccine, IFNα therapy, IL2 therapy, anti-CTLA4therapy, and anti-angiogenic therapy. Patient specific information shownin Immunotherapy Portion 174 includes the patient's biomarkerinformation relating to various immunotherapies and the patient'stherapy scores calculated from their biomarkers.

Efficacy Predictor Portion 176 a provides information indicative of thepatient's predicted response to anti-PD1 therapy based on patientspecific information presented in Immunotherapy Portion 174. EfficacyPredictor Portion 176 b provides information indicative of the patient'spredicted response to anti-CTLA4 therapy based on patient specificinformation presented in Immunotherapy Portion 174.

Targeted Therapy Selection Portion 178 provides patient specificinformation associated with a targeted therapy and informationindicative of the patient's predicted response to the targeted therapy.Targeted Therapy Selection Portion 178 provides such information forsunitinib (SU), imatinib (IM), vemurafenib (VER), dabrafenib (DAB),trametinib, and pazopanib. Patient specific information shown inTargeted Therapy Selection Portion 178 includes a patient's biomarkerinformation relating to various targeted therapies and the patient'stherapy scores calculated from their biomarkers.

An illustrative implementation of a computer system 1500 that may beused in connection with any of the embodiments of the technologydescribed herein is shown in FIG. 15. The computer system 1500 mayinclude one or more computer hardware processors 1510 and one or morearticles of manufacture that comprise non-transitory computer-readablestorage media (e.g., memory 1520 and one or more non-volatile storagedevices 1530). The processor(s) 1510 may control writing data to andreading data from the memory 1520 and the non-volatile storage device(s)1530 in any suitable manner. To perform any of the functionalitydescribed herein, the processor(s) 1510 may execute one or moreprocessor-executable instructions stored in one or more non-transitorycomputer-readable storage media (e.g., the memory 1520), which may serveas non-transitory computer-readable storage media storingprocessor-executable instructions for execution by the processor(s)1510.

FIG. 2A is a flowchart of an illustrative computer-implemented process200 for determining therapy scores for multiple therapies based onnormalized biomarker scores, in accordance with some embodiments of thetechnology described herein. A therapy score provided herein may beindicative of a patient's response to a particular therapy based on thepatient's normalized biomarker scores for biomarkers associated with theparticular therapy. Process 200 may be performed by any suitablecomputing device(s). For example, process 200 may be performed by alaptop computer, a desktop computer, one or more servers, in a cloudcomputing environment, or in any other suitable way.

Process 200 begins at act 202, where sequencing data for a subject isobtained. Any type of sequencing data may be obtained, for example,sequencing data from transcriptome, exome, and/or genome sequencing of apatient's tumor biopsy. In some embodiments, obtaining sequencing datacomprises obtaining sequencing data from a biological sample obtainedfrom the subject and/or from a database storing such information.Further aspects relating to obtaining sequencing data are provided insection “Sample Analysis” and “Obtaining Biomarker Information”.

Next, process 200 proceeds to act 204, where biomarker informationindicating distribution of values for biomarkers associated withmultiple therapies is accessed. In some embodiments, for each particularone of multiple therapies, information indicating a respectivedistribution of values (in a reference population) for each one of oneor more biomarkers associated with the particular therapy may beaccessed. Such biomarker information may be obtained from one or moredatabases, in some embodiments.

Next, process 200 proceeds to act 206, where normalized biomarker scoresfor the subject are determined using sequencing data obtained at act 202and the biomarker information obtained at act 204. Normalized biomarkerscores for the subject are determined, in some embodiments, using areference subset of biomarkers comprising any number of biomarkers fromany number of reference subjects. In that way, the subject's biomarkerscore is adjusted (e.g., normalized) to a common scale based on adistribution of biomarker values in a reference subset of biomarkers.Further aspects relating to determining normalized biomarker scores areprovided in section “From Biomarker Values To Normalized BiomarkerScores”.

Next, process 200 proceeds to act 208, where therapy scores for eachparticular one of the multiple therapies are determined based onnormalized biomarker scores for the biomarkers associated with the eachparticular one therapy. A therapy score may be calculated using multiplenormalized biomarker scores as a sum, as a weighted sum, using a linearor generalized linear model, using a statistical model, or combinationsthereof. The therapy score may be calculated using any suitable numberof normalized biomarker scores, e.g., 2, 10, 50, or 100 normalizedbiomarker scores. Further aspects relating to determining therapy scoresare provided in section “Predicting Therapy Response”.

Therapy scores for any number of therapies may be output to a user, insome embodiments, by displaying the information to the user in agraphical user interface (GUI), including the information in a report,sending an email to the user, and/or in any other suitable way. Forexample, therapy scores and other patient related information may beprovided to a user in a GUI as shown in FIGS. 9-14.

Systems and methods described herein may be used to assess theeffectiveness of a therapy over time. Such systems and methods involvedetermining an impact score for a candidate therapy indicative of animpact of the candidate therapy on the patient based on the patient'sbiomarker information obtained prior to and subsequent to administrationof the candidate therapy.

FIG. 2B is a flowchart of an illustrative computer-implemented process220 for determining an impact score for a candidate therapy using firstand second normalized biomarker scores, in accordance with someembodiments of the technology described herein. An impact score providedherein is indicative of a patient's response to a candidate therapy overtime based on the patient's normalized biomarker scores obtained before,during and/or after treatment. In some embodiments, a first normalizedbiomarker score may be obtained before treatment and a second normalizedbiomarker score may be obtained during and/or after treatment.

Process 220 begins at act 222, where first sequencing data for a subjectprior to administration of a candidate therapy is obtained. Sequencingdata for a subject prior to treatment includes any sequencing dataobtained for that subject any amount of time prior to treatment. Anytype of sequencing data may be obtained, for example, sequencing datafrom transcriptome, exome, and/or genome sequencing of a patient's tumorbiopsy. Sequencing data for the subject may be obtained minutes, days,months, or years prior to treatment. Further aspects relating toobtaining sequencing data are provided in section “Sample Analysis”.

Next, process 220 proceeds to act 224, where second sequencing data fora subject subsequent to administration of a candidate therapy isobtained. Sequencing data for a subject subsequent to treatment includesany sequencing data obtained for that subject any amount of timesubsequent to treatment. Sequencing data for the subject may be obtainedminutes, days, months, or years subsequent to treatment. The secondsequencing data may be a different type of sequencing data than thefirst sequencing data obtained prior to treatment. Further aspectsrelating to obtaining sequencing data are provided in section “SampleAnalysis”.

Next, process 220 proceeds to act 226, where biomarker informationindicating a distribution of values for each of multiple biomarkersassociated with the candidate therapy is accessed. Accessing biomarkerinformation includes obtaining biomarker information associated with thecandidate therapy from a variety of sources including from one or moredatabases. Biomarker information associated with the candidate therapymay be obtained from a subject prior to administration of a therapyand/or after administration of a therapy.

Next, process 220 proceeds to act 228, where first and second normalizedbiomarker scores for the subject are determined using first and secondsequencing data and biomarker information. First and second normalizedbiomarker scores for the subject are determined, in some embodiments,using a reference subset of biomarkers comprising sets of biomarkervalues for the same biomarkers in multiple reference subjects. In thatway, the subject's first and second biomarker score is adjusted (e.g.,normalized) to a common scale based on a distribution of biomarkervalues in a reference subset of biomarkers. Further aspects relating todetermining normalized biomarker scores are provided in section “FromBiomarker Values To Normalized Biomarker Scores”.

Next, process 220 proceeds to act 230, where an impact score for thecandidate therapy is determined based on first and second normalizedbiomarker scores. Such impact scores, in some embodiments, may beindicative of efficacy of the candidate therapy. In some embodiments,impact scores may be used to select an additional therapy, stopadministration of an ongoing therapy, and/or adjust how an ongoingtherapy is being administered for the patient. Further aspects relatingto determining impact scores are provided in section “Impact Scores”.

Impact scores for any number and/or any type of candidate therapies maybe output to a user, in some embodiments, by displaying the informationto the user in a graphical user interface (GUI), including theinformation in a report, sending an email to the user, and/or in anyother suitable way. For example, impact scores and other patient relatedinformation may be provided to a user in a GUI as shown in FIGS. 9-14.

Systems and methods described herein provide a multiple biomarkeranalysis that provides a more accurate prediction of a patient'sresponse to therapy than that provided by a single biomarker analysis.

FIG. 2C is a flowchart of an illustrative computer-implemented process240 for determining therapy scores for at least two selected therapiesbased on respective normalized biomarker scores for at least threebiomarkers, in accordance with some embodiments of the technologydescribed herein. Therapy scores may be determined for selectedtherapies of any suitable type. For example, therapy scores may bedetermined for an immune checkpoint blockade therapy (e.g., anti-PD1therapy) and a kinase inhibitor therapy (e.g., Sunitinib). In anotherexample, therapy scores may be determined for two different immunecheckpoint blockade therapies (e.g., anti-PD1 therapy and anti-CTLA4therapy). Therapy scores may also be determined using any type of threebiomarkers. For example, therapy scores may be determined from at leastthree different genetic biomarkers or therapy scores may be determinedfrom a genetic biomarker, a cellular biomarker, and an expressionbiomarker.

Process 240 begins at act 242, where sequencing data for a subject isobtained. Any type of sequencing data may be obtained, for example,sequencing data from transcriptome, exome, and/or genome sequencing of apatient's tumor biopsy. In some embodiments, obtaining sequencing datacomprises obtaining sequencing data from a biological sample obtainedfrom the subject and/or from a database storing such information.Further aspects relating to obtaining sequencing data are provided insection “Sample Analysis”.

Next, process 240 proceeds to act 244, where biomarker informationindicating distribution of values for the at least three biomarkersassociated with the at least two therapies is accessed. For eachtherapy, information indicating a distribution of values for each of atleast three biomarkers associated with each particular therapy may beaccessed. Thus, in some embodiments, at least six distributions ofvalues may be accessed (e.g., at least three biomarker valuedistributions for three biomarkers associated with a first selectedtherapy and at least three biomarker value distributions for threebiomarkers associated with a second selected therapy). Accessingbiomarker information may include obtaining biomarker information from avariety of sources including one or more databases.

Next, process 240 proceeds to act 246, where first and second sets ofnormalized biomarker scores for the subject are determined using thesequencing data obtained at act 242 and biomarker information obtainedat act 244. First and second sets of normalized biomarker scores for thesubject are determined, in some embodiments, using a reference subset ofbiomarkers comprising sets of biomarker values for the same biomarkersin multiple reference subjects. In that way, the subject's first andsecond sets of biomarker scores are adjusted (e.g., normalized) to acommon scale based on a distribution of biomarker values in a referencesubset of biomarkers. Since the first set of biomarkers is associatedwith one therapy and the second set of biomarkers is associated withanother therapy, the first and second sets of normalized biomarkers maydiffer from each other, for example, in number of biomarkers and/ortypes of biomarkers. For example, the first set of normalized biomarkerscores may be associated with a first therapy and the second set ofnormalized biomarker scores may be associated with a second therapy.Further aspects relating to determining normalized biomarker scores areprovided in section “From Biomarker Values To Normalized BiomarkerScores”. Next, process 240 proceeds to act 248, where therapy scores forthe at least two therapies are determined based on at least threenormalized biomarker scores for each therapy. A therapy score may becalculated using the at least three normalized biomarker scores as asum, as a weighted sum, using a linear or generalized linear model,using a statistical model, or combinations thereof. The therapy scoremay be calculated using any suitable number of normalized biomarkerscores, e.g., 2, 10, 50, or 100 normalized biomarker scores. Furtheraspects relating to determining therapy scores are provided in section“Predicting Therapy Response”.

Therapy scores for the at least two therapies and/or biomarkerinformation used for determining therapy scores may be output to a user,in some embodiments, by displaying the information to the user in agraphical user interface (GUI), including the information in a report,sending an email to the user, and/or in any other suitable way. Forexample, therapy scores and other patient related information may beprovided to a user in a GUI as shown in FIGS. 9-14.

Systems and methods described herein provide for determining more thanone therapy score for a particular therapy. For example, a first and asecond therapy score may be determined for a first therapy, and a firstand second therapy score may be determined for a second therapy.

FIG. 2D is a flowchart of an illustrative computer-implemented process260 for determining first and second therapy scores for a first andsecond therapy, respectively, based on normalized biomarker scores, inaccordance with some embodiments of the technology described herein.First and second therapy scores may be determined using differentbiomarkers or different combinations of biomarkers. For example, a firsttherapy score is determined based on a patient's genetic biomarkers anda second therapy score is based on the patient's expression biomarkers.In another example, a first therapy score is determined based on apatient's genetic biomarkers and a second therapy score is based on thepatient's genetic biomarkers and expression biomarkers. First and secondtherapy scores may be determined for different therapies and/ordifferent types of therapies. For example, first and second therapyscores may be determined for an immune checkpoint blockade therapy(e.g., anti-PD1 therapy) and a kinase inhibitor therapy (e.g.,Sunitinib), respectively. In another example, first and second therapytherapy scores may be determined for two different immune checkpointblockade therapies (e.g., anti-PD1 therapy and anti-CTLA4 therapy).

Process 260 begins at act 262, where sequencing data for a subject isobtained. Any type of sequencing data may be obtained, for example,sequencing data from transcriptome, exome, and/or genome sequencing of apatient's tumor biopsy. In some embodiments, obtaining sequencing datacomprises obtaining sequencing data from a biological sample obtainedfrom the subject and/or from a database storing such information.Further aspects relating to obtaining sequencing data are provided insection “Sample Analysis”.

Next, process 260 proceeds to act 264, where biomarker informationindicating distribution of values for biomarkers associated with atleast two therapies. In some embodiments, information indicating adistribution of values is obtained for each of one or more biomarkersassociated with a first therapy, and information indicating adistribution of values is obtained for each of one or more biomarkersassociated with a second therapy different from the first therapy.Accessing biomarker information may include obtaining biomarkerinformation from a variety of sources including, for example, one ormore databases

Next, process 260 proceeds to act 266, where first and second sets ofnormalized biomarker scores for the subject are determined usingsequencing data obtained at act 262 and biomarker information obtainedat act 264. First and second sets of normalized biomarker scores for thesubject are determined, in some embodiments, using a reference subset ofbiomarkers comprising sets of biomarker values for the same biomarkersin multiple reference subjects. In that way, the subject's first andsecond sets of biomarker scores are adjusted (e.g., normalized) to acommon scale based on a distribution of biomarker values in a referencesubset of biomarkers. Since the first set of biomarkers is associatedwith one therapy and the second set of biomarkers is associated withanother therapy, the first and second sets of normalized biomarkers maydiffer from each other, for example, in number of biomarkers and/ortypes of biomarkers. Further aspects relating to determining normalizedbiomarker scores are provided in section “From Biomarker Values ToNormalized Biomarker Scores”.

Next, process 260 proceeds to act 268, where first and second therapyscores for the first and second therapies are determined based onnormalized biomarker scores for each therapy. A therapy score may becalculated using the normalized biomarker scores as a sum, as a weightedsum, using a linear or generalized linear model, using a statisticalmodel, or combinations thereof. The therapy score may be calculatedusing any suitable number of normalized biomarker scores, e.g., 2, 10,50, or 100 normalized biomarker scores. Further aspects relating todetermining therapy scores are provided in section “Predicting TherapyResponse”.

First and second therapy scores for first and second therapies and/orbiomarker information used for determining therapy scores may be outputto a user, in some embodiments, by displaying the information to theuser in a graphical user interface (GUI), including the information in areport, sending an email to the user, and/or in any other suitable way.For example, therapy scores and other patient related information may beprovided to a user in a GUI as shown in FIGS. 9-14.

Systems and methods described herein may be used to select patients fora clinical trial for a particular therapy based on the patient'spredicted response to that therapy determined using the patient'sbiomarkers as described herein. The systems and methods described hereinmay be used to identify a patient as a member of a cohort forparticipation in a clinical trial.

FIG. 2E is a flowchart of an illustrative computer-implemented process280 for identifying a subject as a member of a cohort using normalizedbiomarker scores, in accordance with some embodiments of the technologydescribed herein. A subject may be identified as a member of a cohortfor a clinical trial of any type of therapy, for example, achemotherapy, an immunotherapy, an antibody therapy, and/or anycombination thereof. The patient may be identified as a member of acohort that will be administered the treatment or as a member of acohort that will be administered a placebo. In some embodiments, thepatient may be not be identified as a member of a cohort, and thus maybe excluded from participation in a clinical trial. Patients may beexcluded from a clinical trial, in some embodiments, if those patientshave been predicted to have an adverse reaction to a therapy determinedusing the patient's biomarkers as described herein and/or the patient'sgene expression data as described in International patent applicationnumber PCT/US18/37018, entitled “Systems and Methods for IdentifyingResponders and Non-Responders to Immune Checkpoint Blockade Therapy,”filed Jun. 12, 2018, the entire contents of which are incorporatedherein by reference.

Process 280 begins at act 282, where sequencing data for a subject isobtained. Any type of sequencing data may be obtained, for example,sequencing data from transcriptome, exome, and/or genome sequencing of apatient's tumor biopsy. In some embodiments, obtaining sequencing datacomprises obtaining sequencing data from a biological sample obtainedfrom the subject and/or from a database storing such information.Further aspects relating to obtaining sequencing data are provided insection “Sample Analysis”.

Next, process 280 proceeds to act 284, where biomarker informationindicating a distribution of values for each of one or more biomarkersassociated with a therapy is accessed. Accessing biomarker informationmay include obtaining biomarker information from a variety of sources,for example, one or more databases.

Next, process 280 proceeds to act 286, where normalized biomarker scoresfor the subject are determined using sequencing data and biomarkerinformation. Normalized biomarker scores for the subject are determined,in some embodiments, using a reference subset of biomarkers comprisingsets of biomarker values for the same biomarkers in multiple referencesubjects. In that way, the subject's biomarker score is adjusted (e.g.,normalized) to a common scale based on a distribution of biomarkervalues in a reference subset of biomarkers. Further aspects relating todetermining normalized biomarker scores are provided in section “FromBiomarker Values To Normalized Biomarker Scores”.

Next, process 280 proceeds to act 288, where a subject is identified asa member of a cohort for participating in a clinical trial usingbiomarker scores. An identified subject, in some embodiments, may be asubject that is likely to respond positively to the therapy beingadministered in the clinical trial. Such information may be output to auser, in some embodiments, by displaying the information to the user ina graphical user interface (GUI), including the information in a report,sending an email to the user, and/or in any other suitable way.

In this way, a patient can be identified and selected for participationin a clinical trial based on the patient's biomarker scores. Patientscan also be identified for exclusion from the clinical trial, forexample, patients predicted not likely to respond positively to thetherapy and/or patients predicted to have an adverse reaction to thetherapy.

Presentation of Predicted Therapy Response or Impact Score

In some embodiments, a software program may provide a user with a visualrepresentation presenting information related to a patient's biomarkervalues (e.g., a biomarker score, and/or a therapy score, and/or animpact score), and predicted efficacy or determined efficacy of one ormore therapies using a graphical user interface (GUI).

In response to being launched, the interactive GUI may provide the userof the software program with a visual representation of a patient'sbiomarker values and/or additional information related to the biomarker.FIGS. 6A-6C are screenshots presenting such information to a user of thesoftware program.

FIG. 6A is a screenshot presenting a patient's biomarker informationassociated with different immunotherapies that may be used to treat thepatient. Shading reflects normalized biomarker value in terms ofgradient from −1 to 1. Shading intensity increasing as the biomarkervalue is increased. Shading with lines is assigned to positive biomarkervalues to distinguish them from negative biomarker values. Numeric“weight” of a biomarker is reflected in the width of the block withlarger block width indicating a higher numeric weight.

As shown in FIG. 6A, a greater number of biomarkers with positive scoreswere calculated for anti-PD1 therapy indicating a predicted positivetherapeutic effect of anti-PD1 therapy for a patient. By contrast, agreater number of biomarkers with negative scores were calculated foranti-VEGF therapy indicating a predicted negative therapeutic effect ofanti-VEGF therapy for a patient. Numbers of positive biomarkers andnegative biomarkers for a particular therapy may be similar for apatient. In such a case, the therapeutic effects of that therapy for thepatient may not be predicted (i.e., may not be accurately predicted).Medium biomarker values for a particular therapy may also indicate thatthe therapeutic effects of that therapy for the patient may not bepredicted (i.e., may not be accurately predicted).

FIG. 6B is a visual representation illustrating therapy scorescalculated using normalized biomarker values shown in FIG. 6A. Negativetherapy scores are shown on the left side of the y-axis, and positivetherapy scores are shown on the right side of the y-axis. Positivetherapy scores are also differentiated from negative therapy scores byshading with lines.

A user may interact with the GUI to obtain additional information abouta biomarker. FIG. 6C is a screenshot presenting information related toeach biomarker and patient specific information related to thatbiomarker. Information presented includes, from left to right, a blockrepresenting each biomarker, a description of the biomarker, a graphshowing the distribution of biomarker values, and a general descriptionof the biomarker value as “high,” “low,” or “neutral”. The arrow in thegraph indicates the patient's biomarker value. In some embodiments anormalized biomarker score may be labeled as a high score when thenormalized biomarker score is in the top threshold percent (e.g., 1%,5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%) of a distribution ofvalues. In some embodiments, a normalized biomarker score may be labeledas a low score when the normalized biomarker score is in the bottomthreshold percent (e.g., 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%,50%) of a distribution of values. In certain embodiments, a normalizedbiomarker score may be considered neutral if it is not in the topthreshold or the bottom threshold of a distribution of values.

FIG. 9 is a graphic illustrating different types of screens that may beshown to a user of the software program. Each of the different screensillustrated in FIG. 9 may be used to present different types ofinformation to the user. A screenshot of a control screen of thesoftware program is shown in the middle of FIG. 9. The control screenincludes portions for presenting information relating to treatmentselection, tumor properties, and clinical evidence of treatment efficacyand is described further with respect to FIGS. 10-15.

A user may interact with the control screen to obtain additionalinformation about, for example, immunotherapy selection, targetedtherapy selection, combination therapy design, tumor properties andtumor microenvironment, clinical evidence of targeted therapy efficacy,and clinical evidence of immunotherapy efficacy. The user may select aportion of the control screen (e.g., the immunotherapy portion) to viewone or more additional screens presenting information relating to theselected portion. As shown in FIG. 9, arrows point from a portion of thecontrol screen that may be selected toward the screens presentingadditional information related to the selected portion.

For example, the user may select the immunotherapy selection portion ofthe control screen to view one or more screens presenting informationrelating to various immunotherapies, biomarkers associated with animmunotherapy (e.g., genetic biomarkers, cellular biomarkers, andexpression biomarkers), immune cell properties of the patient's tumor,and clinical trials (e.g., information from and/or regarding publishedclinical trials and ongoing clinical trials).

In another example, the user may select the targeted therapy selectionportion of the control screen to view one or more screens presentinginformation relating to various targeted therapies, biomarkersassociated with targeted therapies (e.g., genetic biomarkers, cellularbiomarkers, and/or expression biomarkers), properties of the patient'stumor associated with the targeted therapy, and clinical trials (e.g.,published clinical trials and ongoing clinical trials).

In another example, the user may select the molecular-functionalportrait (MF profile) portion of the control screen to view one or morescreens presenting information relating to the patient's tumormicroenvironment. Such information may include information about tumorproperties (e.g., proliferation rate), angiogenesis, metastasis,cellular composition, cancer associated fibroblasts, pro-tumor immuneenvironment, and anti-tumor immune environment.

In yet another example, the user may select the clinical evidence oftreatment efficacy portion of the control screen to view one or morescreens presenting information relating to a therapy (e.g., animmunotherapy or targeted therapy). Such information may includedescription of the therapy, therapy efficacy, potential adverse effects,related publications, treatment regimen, and patient survival data.

In a further example, the user may select a portion of the controlscreen to view one or more screens associated with an impact score forone or more candidate therapies, wherein the impact score is indicativeof response of the subject to administration of the one or morecandidate therapies.

A user of the software program may interact with the GUI to log into thesoftware program. The user may select a stored report to view a screenpresenting information relating to the selected report. The user mayselect the create new report portion to view a screen for creating a newreport.

FIG. 10 is a screenshot presenting the selected patient's reportincluding information related to the patient's sequencing data, thepatient, and the patient's cancer. The therapy biomarkers portion (asshown in the left panel) presents information related to availabletherapies (e.g., immunotherapies and targeted therapies) and theirpredicted efficacy in the selected patient. Additional predictions ofthe efficacy of a therapy in the patient are provided in the machinepredictor portion and additional portion (as shown in the left panel).The MF profile portion presents information relating to the molecularcharacteristics of a tumor including tumor genetics, pro-tumormicroenvironment factors, and anti-tumor immune response factors (asshown in the middle panel). The clinical trials portion providesinformation relating to clinical trials (as shown in the right panel).The monotherapy or combinational therapy portion (as shown in the middlepanel) may be selected by the user to interactively design apersonalized treatment for a patient.

A user may select various portions of the screen to view additionalinformation. For example, a user may select anti-PD1 in theimmunotherapy biomarkers portion of the screen (as shown in the leftpanel) to view information relating to anti-PD1 treatment includingbiomarkers associated with anti-PD1 and tumor cell processes associatedwith anti-PD1 treatment.

FIG. 11 is a screenshot presenting information related to anti-PD1immunotherapy provided in response to selecting anti-PD1 immunotherapy(as shown by highlighting) in the immunotherapy biomarkers portion ofthe screen (as shown in the left panel). Information relating tobiomarkers associated with anti-PD1 immunotherapy is provided in thebiomarkers portion (as shown in the right panel). The biomarkers portionpresents genetic biomarkers, cellular biomarkers, and expressionbiomarkers, as well as patient specific information related to thosebiomarkers.

The user may select any one of the biomarkers presented in thebiomarkers markers portion to view additional information relating tothat biomarker including general information about the selectedbiomarker, patient specific information relating to the selectedbiomarker, information relating to tumor molecular processes associatedwith the selected biomarker, and treatment related informationassociated with the selected biomarker.

In response to selection by a user, the selected biomarker may bevisually highlighted. As a set of non-limiting examples, a “visuallyhighlighted” element may be highlighted through a difference in font(e.g., by italicizing, bolding, and/or underlining), by surrounding thesection with a visual object (e.g., a box), by “popping” the element out(e.g., by increasing the zoom for that element), by changing the colorof an element, by shading the element, by incorporation of movement intothe element (e.g., by causing the element to move), any combination ofthe foregoing in a portion or the whole of the element, or in any othersuitable way. FIG. 12 is a screenshot presenting the mutational burdenbiomarker (as shown by highlighting) was selected by the user. The usermay select another portion of the mutational burden biomarker to view ascreen presenting information relating to the mutational burdenbiomarker such as relevant publications.

FIG. 13 is a screenshot presenting information relating to themutational burden biomarker (as shown in the middle panel) provided inresponse to the user selecting the mutational burden biomarker. Theinformation may include a description of the biomarker, how thebiomarker was calculated, the patient's particular biomarker valuecompared to other patients (as shown in a histogram), and informationfrom publications relating to the selected biomarker.

The system allows a user to interactively view biomarker information asit relates to a predicted response to a therapy. Clinical evidence oftreatment efficacy for a therapy (e.g., an immunotherapy or a targetedtherapy) may be interactively viewed by the user.

FIG. 14 is a screenshot presenting clinical trial data relating toanti-PD1 therapy effectivity in patients having stage IV metastaticmelanoma (as shown in the right panel) provided in response to the userselecting anti-PD1 immunotherapy (as shown in the left panel).

Therapeutics and Methods of Therapy

In certain methods or systems described herein, no recommendation ismade regarding administration of a therapy to a subject (e.g., a human).In certain methods described herein, an effective amount of anti-cancertherapy described herein may be administered or recommended foradministration to a subject (e.g., a human) in need of the treatment viaa suitable route (e.g., intravenous administration).

The subject to be treated by the methods described herein may be a humanpatient having, suspected of having, or at risk for a cancer. Examplesof a cancer include, but are not limited to, melanoma, lung cancer,brain cancer, breast cancer, colorectal cancer, pancreatic cancer, livercancer, prostate cancer, skin cancer, kidney cancer, bladder cancer, orprostate cancer. The subject to be treated by the methods describedherein may be a mammal (e.g., may be a human). Mammals may include, butare not limited to: farm animals (e.g., livestock), sport animals,laboratory animals, pets, primates, horses, dogs, cats, mice, and rats.

A subject having a cancer may be identified by routine medicalexamination, e.g., laboratory tests, biopsy, PET scans, CT scans, orultrasounds. A subject suspected of having a cancer might show one ormore symptoms of the disorder, e.g., unexplained weight loss, fever,fatigue, cough, pain, skin changes, unusual bleeding or discharge,and/or thickening or lumps in parts of the body. A subject at risk for acancer may be a subject having one or more of the risk factors for thatdisorder. For example, risk factors associated with cancer include, butare not limited to, (a) viral infection (e.g., herpes virus infection),(b) age, (c) family history, (d) heavy alcohol consumption, (e) obesity,and (f) tobacco use.

Any anti-cancer therapy or anti-cancer therapeutic agent may be used inconjunction with the methods and systems described herein. In someembodiments, an anti-cancer therapeutic agent is an antibody, animmunotherapy, a molecular targeted therapy, a radiation therapy, asurgical therapy, and/or a chemotherapy.

Examples of the antibody anti-cancer agents include, but are not limitedto, alemtuzumab (Campath), trastuzumab (Herceptin), Ibritumomab tiuxetan(Zevalin), Brentuximab vedotin (Adcetris), Ado-trastuzumab emtansine(Kadcyla), blinatumomab (Blincyto), Bevacizumab (Avastin), Cetuximab(Erbitux), ipilimumab (Yervoy), nivolumab (Opdivo), pembrolizumab(Keytruda), atezolizumab (Tecentriq), avelumab (Bavencio), durvalumab(Imfinzi), and panitumumab (Vectibix).

Examples of an immunotherapy include, but are not limited to, a PD-1inhibitor or a PD-L1 inhibitor, a CTLA-4 inhibitor, adoptive celltransfer, therapeutic cancer vaccines, oncolytic virus therapy, T-celltherapy, and immune checkpoint inhibitors.

Examples of a molecular targeted therapy include, but are not limitedto: Uprosertib, Alectinib, Crizotinib, Alisertib, Barasertib,Gilteritinib, Navitoclax, Bosutinib, Dasatinib, Nilotinib, Ponatinib,Imatinib, Dabrafenib, Vemurafenib, Encorafenib, Acalabrutinib,Ibrutinib, Verapamil, Tacrolimus, Abemaciclib, Ribociclib, Palbociclib,Celecoxib, Apricoxib, Selinexor, Plerixafor, Pinometostat, Rociletinib,Pyrotinib, Erlotinib, Gefitinib, Afatinib, Osimertinib, Varlitinib,Icotinib, Lapatinib, Neratinib, Tazemetostat, Tipifarnib, Dovitinib,Lucitanib, Erdafitinib, Crenolanib, Atorvastatin, Onalespib, Enasidenib,Sitagliptin, Ruxolitinib, Tofacitinib, Idasanutlin, Selumetinib,Trametinib, Cobimetinib, Binimetinib, Foretinib, Capmatinib, Tivantinib,Volitinib, Vistusertib, Everolimus, Sirolimus, Torkinib, Temsirolimus,Ridaforolimus, Metformin, Apitolisib, Dactolisib, Brontictuzumab,Omaveloxolone, Dacomitinib, Sapitinib, Poziotinib, Cabozantinib,Regorafenib, Lestaurtinib, Midostaurin, Nintedanib, Pexidartinib,Quizartinib, Sorafenib, Sunitinib, Vandetanib, Entrectinib, Pazopanib,Masitinib, Anlotinib, Brigatinib, Olaparib, Apatinib, Niraparib,Rucaparib, Veliparib, Roflumilast, Idelalisib, Copanlisib, Buparlisib,Taselisib, Pictilisib, Umbralisib, Duvelisib, Alpelisib, Volasertib,Vismodegib, Sonidegib, Saracatinib, Entospletinib, Fostamatinib,Cerdulatinib, Larotrectinib, Auranofin, Axitinib, Cediranib, Lenvatinib,and Alvocidib.

Examples of radiation therapy include, but are not limited to, ionizingradiation, gamma-radiation, neutron beam radiotherapy, electron beamradiotherapy, proton therapy, brachytherapy, systemic radioactiveisotopes, and radiosensitizers.

Examples of a surgical therapy include, but are not limited to, acurative surgery (e.g., tumor removal surgery), a preventive surgery, alaparoscopic surgery, and a laser surgery.

Examples of the chemotherapeutic agents include, but are not limited to,Carboplatin or Cisplatin, Docetaxel, Gemcitabine, Nab-Paclitaxel,Paclitaxel, Pemetrexed, and Vinorelbine.

Additional examples of chemotherapy include, but are not limited to,Platinating agents, such as Carboplatin, Oxaliplatin, Cisplatin,Nedaplatin, Satraplatin, Lobaplatin, Triplatin, Tetranitrate,Picoplatin, Prolindac, Aroplatin and other derivatives; Topoisomerase Iinhibitors, such as Camptothecin, Topotecan, irinotecan/SN38, rubitecan,Belotecan, and other derivatives; Topoisomerase II inhibitors, such asEtoposide (VP-16), Daunorubicin, a doxorubicin agent (e.g., doxorubicin,doxorubicin hydrochloride, doxorubicin analogs, or doxorubicin and saltsor analogs thereof in liposomes), Mitoxantrone, Aclarubicin, Epirubicin,Idarubicin, Amrubicin, Amsacrine, Pirarubicin, Valrubicin, Zorubicin,Teniposide and other derivatives; Antimetabolites, such as Folic family(Methotrexate, Pemetrexed, Raltitrexed, Aminopterin, and relatives orderivatives thereof); Purine antagonists (Thioguanine, Fludarabine,Cladribine, 6-Mercaptopurine, Pentostatin, clofarabine, and relatives orderivatives thereof) and Pyrimidine antagonists (Cytarabine,Floxuridine, Azacitidine, Tegafur, Carmofur, Capacitabine, Gemcitabine,hydroxyurea, 5-Fluorouracil (5FU), and relatives or derivativesthereof); Alkylating agents, such as Nitrogen mustards (e.g.,Cyclophosphamide, Melphalan, Chlorambucil, mechlorethamine, Ifosfamide,mechlorethamine, Trofosfamide, Prednimustine, Bendamustine, Uramustine,Estramustine, and relatives or derivatives thereof); nitrosoureas (e.g.,Carmustine, Lomustine, Semustine, Fotemustine, Nimustine, Ranimustine,Streptozocin, and relatives or derivatives thereof); Triazenes (e.g.,Dacarbazine, Altretamine, Temozolomide, and relatives or derivativesthereof); Alkyl sulphonates (e.g., Busulfan, Mannosulfan, Treosulfan,and relatives or derivatives thereof); Procarbazine; Mitobronitol, andAziridines (e.g., Carboquone, Triaziquone, ThioTEPA,triethylenemalamine, and relatives or derivatives thereof); Antibiotics,such as Hydroxyurea, Anthracyclines (e.g., doxorubicin agent,daunorubicin, epirubicin and relatives or derivatives thereof);Anthracenediones (e.g., Mitoxantrone and relatives or derivativesthereof); Streptomyces family antibiotics (e.g., Bleomycin, Mitomycin C,Actinomycin, and Plicamycin); and ultraviolet light.

“An effective amount” as used herein refers to the amount of each activeagent required to confer therapeutic effect on the subject, either aloneor in combination with one or more other active agents. Effectiveamounts vary, as recognized by those skilled in the art, depending onthe particular condition being treated, the severity of the condition,the individual patient parameters including age, physical condition,size, gender and weight, the duration of the treatment, the nature ofconcurrent therapy (if any), the specific route of administration andlike factors within the knowledge and expertise of the healthpractitioner. These factors are well known to those of ordinary skill inthe art and can be addressed with no more than routine experimentation.It is generally preferred that a maximum dose of the individualcomponents or combinations thereof be used, that is, the highest safedose according to sound medical judgment. It will be understood by thoseof ordinary skill in the art, however, that a patient or clinician mayinsist upon a lower dose or tolerable dose for medical reasons,psychological reasons, or for virtually any other reason(s).

Empirical considerations, such as the half-life of a therapeuticcompound, generally contribute to the determination of the dosage. Forexample, antibodies that are compatible with the human immune system,such as humanized antibodies or fully human antibodies, may be used toprolong half-life of the antibody and to prevent the antibody beingattacked by the host's immune system. Frequency of administration may bedetermined and adjusted over the course of therapy, and is generally(but not necessarily) based on treatment, and/or suppression, and/oramelioration, and/or delay of a cancer. Alternatively, sustainedcontinuous release formulations of an anti-cancer therapeutic agent maybe appropriate. Various formulations and devices for achieving sustainedrelease are known in the art.

In some embodiments, dosages for an anti-cancer therapeutic agent asdescribed herein may be determined empirically in individuals who havebeen administered one or more doses of the anti-cancer therapeuticagent. Individuals may be administered incremental dosages of theanti-cancer therapeutic agent. To assess efficacy of an administeredanti-cancer therapeutic agent, one or more aspects of a cancer (e.g.,tumor formation or tumor growth) may be analyzed.

Generally, for administration of any of the anti-cancer antibodiesdescribed herein, an initial candidate dosage may be about 2 mg/kg. Forthe purpose of the present disclosure, a typical daily dosage mightrange from about any of 0.1 μg/kg to 3 μg/kg to 30 μg/kg to 300 μg/kg to3 mg/kg, to 30 mg/kg to 100 mg/kg or more, depending on the factorsmentioned above. For repeated administrations over several days orlonger, depending on the condition, the treatment is sustained until adesired suppression or amelioration of symptoms occurs or untilsufficient therapeutic levels are achieved to alleviate a cancer, or oneor more symptoms thereof. An exemplary dosing regimen comprisesadministering an initial dose of about 2 mg/kg, followed by a weeklymaintenance dose of about 1 mg/kg of the antibody, or followed by amaintenance dose of about 1 mg/kg every other week. However, otherdosage regimens may be useful, depending on the pattern ofpharmacokinetic decay that the practitioner (e.g., a medical doctor)wishes to achieve. For example, dosing from one-four times a week iscontemplated. In some embodiments, dosing ranging from about 3 μg/mg toabout 2 mg/kg (such as about 3 μg/mg, about 10 μg/mg, about 30 μg/mg,about 100 μg/mg, about 300 μg/mg, about 1 mg/kg, and about 2 mg/kg) maybe used. In some embodiments, dosing frequency is once every week, every2 weeks, every 4 weeks, every 5 weeks, every 6 weeks, every 7 weeks,every 8 weeks, every 9 weeks, or every 10 weeks; or once every month,every 2 months, or every 3 months, or longer. The progress of thistherapy may be monitored by conventional techniques and assays and/or bymonitoring the progress of the disease or cancer as described herein.The dosing regimen (including the therapeutic used) may vary over time.

When the anti-cancer therapeutic agent is not an antibody, it may beadministered at the rate of about 0.1 to 300 mg/kg of the weight of thepatient divided into one to three doses, or as disclosed herein. In someembodiments, for an adult patient of normal weight, doses ranging fromabout 0.3 to 5.00 mg/kg may be administered. The particular dosageregimen, e.g., dose, timing, and/or repetition, will depend on theparticular subject and that individual's medical history, as well as theproperties of the individual agents (such as the half-life of the agent,and other considerations well known in the art).

For the purpose of the present disclosure, the appropriate dosage of ananti-cancer therapeutic agent will depend on the specific anti-cancertherapeutic agent(s) (or compositions thereof) employed, the type andseverity of cancer, whether the anti-cancer therapeutic agent isadministered for preventive or therapeutic purposes, previous therapy,the patient's clinical history and response to the anti-cancertherapeutic agent, and the discretion of the attending physician.Typically the clinician will administer an anti-cancer therapeuticagent, such as an antibody, until a dosage is reached that achieves thedesired result.

Administration of an anti-cancer therapeutic agent can be continuous orintermittent, depending, for example, upon the recipient's physiologicalcondition, whether the purpose of the administration is therapeutic orprophylactic, and other factors known to skilled practitioners. Theadministration of an anti-cancer therapeutic agent (e.g., an anti-cancerantibody) may be essentially continuous over a preselected period oftime or may be in a series of spaced dose, e.g., either before, during,or after developing cancer.

As used herein, the term “treating” refers to the application oradministration of a composition including one or more active agents to asubject, who has a cancer, a symptom of a cancer, or a predispositiontoward a cancer, with the purpose to cure, heal, alleviate, relieve,alter, remedy, ameliorate, improve, or affect the cancer or one or moresymptoms of the cancer, or the predisposition toward a cancer. In someembodiments, the methods and systems herein may comprise recommendationof a treatment rather than treatment itself. In some embodiments, norecommendation of a treatment will be made. In certain embodiments, oneor more potential treatments may be “ranked” or compared according totheir predicted efficacy and/or subject or patient outcome. In certainembodiments, one or more potential treatments will not be “ranked” orcompared according to their predicted efficacy and/or subject or patientoutcome. In some embodiments, information about a therapy (e.g., thetherapy score) for a patient will be outputted. In specific embodiments,such information may be outputted to a user (e.g., a doctor orclinician).

Alleviating a cancer includes delaying the development or progression ofthe disease, or reducing disease severity (e.g., by at least oneparameter). Alleviating the disease does not necessarily requirecurative results. As used therein, “delaying” the development of adisease (e.g., a cancer) means to defer, hinder, slow, retard,stabilize, and/or postpone progression of the disease. This delay can beof varying lengths of time, depending on the history of the diseaseand/or individuals being treated. A method that “delays” or alleviatesthe development or progress of a disease, or delays the onset of one ormore complications of the disease, is a method that reduces probabilityof developing one or more symptoms of the disease in a given time frameand/or reduces extent of the symptoms in a given time frame, whencompared to not using the method. Such comparisons are typically basedon clinical studies, using a number of subjects sufficient to give astatistically significant result.

“Development” or “progression” of a disease means initial manifestationsand/or ensuing progression of the disease. Development of the diseasecan be detected and assessed using clinical techniques known in the art.Alternatively or in addition to the clinical techniques known in theart, development of the disease may be detectable and assessed based onbiomarkers described herein. However, development also refers toprogression that may be undetectable. For purpose of this disclosure,development or progression refers to the biological course of thesymptoms. “Development” includes occurrence, recurrence, and onset. Asused herein “onset” or “occurrence” of a cancer includes initial onsetand/or recurrence.

In some embodiments, the anti-cancer therapeutic agent (e.g., anantibody) described herein is administered to a subject in need of thetreatment at an amount sufficient to reduce cancer (e.g., tumor) growthby at least 10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% orgreater). In some embodiments, the anti-cancer therapeutic agent (e.g.,an antibody) described herein is administered to a subject in need ofthe treatment at an amount sufficient to reduce cancer cell number ortumor size by at least 10% (e.g., 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%or more). In other embodiments, the anti-cancer therapeutic agent isadministered in an amount effective in altering cancer type (e.g., froma more severe to a less severe type; or from a worse prognosis to abetter prognosis). Alternatively, the anti-cancer therapeutic agent isadministered in an amount effective in reducing tumor formation, size,or metastasis.

Conventional methods, known to those of ordinary skill in the art ofmedicine, may be used to administer the anti-cancer therapeutic agent tothe subject, depending upon the type of disease to be treated or thesite of the disease. The anti-cancer therapeutic agent can also beadministered via other conventional routes, e.g., administered orally,parenterally, by inhalation spray, topically, rectally, nasally,buccally, vaginally, or via an implanted reservoir. The term“parenteral” as used herein includes subcutaneous, intracutaneous,intravenous, intramuscular, intraarticular, intraarterial,intrasynovial, intrasternal, intrathecal, intralesional, andintracranial injection or infusion techniques. In addition, ananti-cancer therapeutic agent may be administered to the subject viainjectable depot routes of administration such as using 1-, 3-, or6-month depot injectable or biodegradable materials and methods.

Injectable compositions may contain various carriers such as vegetableoils, dimethylactamide, dimethyformamide, ethyl lactate, ethylcarbonate, isopropyl myristate, ethanol, and polyols (e.g., glycerol,propylene glycol, liquid polyethylene glycol, and the like). Forintravenous injection, water soluble anti-cancer therapeutic agents canbe administered by the drip method, whereby a pharmaceutical formulationcontaining the antibody and a physiologically acceptable excipients isinfused. Physiologically acceptable excipients may include, for example,5% dextrose, 0.9% saline, Ringer's solution, and/or other suitableexcipients. Intramuscular preparations, e.g., a sterile formulation of asuitable soluble salt form of the anti-cancer therapeutic agent, can bedissolved and administered in a pharmaceutical excipient such asWater-for-Injection, 0.9% saline, and/or 5% glucose solution.

In one embodiment, an anti-cancer therapeutic agent is administered viasite-specific or targeted local delivery techniques. Examples ofsite-specific or targeted local delivery techniques include variousimplantable depot sources of the agent or local delivery catheters, suchas infusion catheters, an indwelling catheter, or a needle catheter,synthetic grafts, adventitial wraps, shunts and stents or otherimplantable devices, site specific carriers, direct injection, or directapplication. See, e.g., PCT Publication No. WO 00/53211 and U.S. Pat.No. 5,981,568, the contents of each of which are incorporated byreference herein for this purpose.

Targeted delivery of therapeutic compositions containing an antisensepolynucleotide, expression vector, or subgenomic polynucleotides canalso be used. Receptor-mediated DNA delivery techniques are describedin, for example, Findeis et al., Trends Biotechnol. (1993) 11:202; Chiouet al., Gene Therapeutics: Methods And Applications Of Direct GeneTransfer (J. A. Wolff, ed.) (1994); Wu et al., J. Biol. Chem. (1988)263:621; Wu et al., J. Biol. Chem. (1994) 269:542; Zenke et al., Proc.Natl. Acad. Sci. USA (1990) 87:3655; Wu et al., J. Biol. Chem. (1991)266:338. The contents of each of the foregoing are incorporated byreference herein for this purpose.

Therapeutic compositions containing a polynucleotide may be administeredin a range of about 100 ng to about 200 mg of DNA for localadministration in a gene therapy protocol. In some embodiments,concentration ranges of about 500 ng to about 50 mg, about 1 μg to about2 mg, about 5 μg to about 500 μg, and about 20 μg to about 100 μg of DNAor more can also be used during a gene therapy protocol.

Therapeutic polynucleotides and polypeptides can be delivered using genedelivery vehicles. The gene delivery vehicle can be of viral ornon-viral origin (e.g., Jolly, Cancer Gene Therapy (1994) 1:51; Kimura,Human Gene Therapy (1994) 5:845; Connelly, Human Gene Therapy (1995)1:185; and Kaplitt, Nature Genetics (1994) 6:148). The contents of eachof the foregoing are incorporated by reference herein for this purpose.Expression of such coding sequences can be induced using endogenousmammalian or heterologous promoters and/or enhancers. Expression of thecoding sequence can be either constitutive or regulated.

Viral-based vectors for delivery of a desired polynucleotide andexpression in a desired cell are well known in the art. Exemplaryviral-based vehicles include, but are not limited to, recombinantretroviruses (see, e.g., PCT Publication Nos. WO 90/07936; WO 94/03622;WO 93/25698; WO 93/25234; WO 93/11230; WO 93/10218; WO 91/02805; U.S.Pat. Nos. 5,219,740 and 4,777,127; GB Patent No. 2,200,651; and EPPatent No. 0 345 242), alphavirus-based vectors (e.g., Sindbis virusvectors, Semliki forest virus (ATCC VR-67; ATCC VR-1247), Ross Rivervirus (ATCC VR-373; ATCC VR-1246) and Venezuelan equine encephalitisvirus (ATCC VR-923; ATCC VR-1250; ATCC VR 1249; ATCC VR-532)), andadeno-associated virus (AAV) vectors (see, e.g., PCT Publication Nos. WO94/12649, WO 93/03769; WO 93/19191; WO 94/28938; WO 95/11984 and WO95/00655). Administration of DNA linked to killed adenovirus asdescribed in Curiel, Hum. Gene Ther. (1992) 3:147 can also be employed.The contents of each of the foregoing are incorporated by referenceherein for this purpose.

Non-viral delivery vehicles and methods can also be employed, including,but not limited to, polycationic condensed DNA linked or unlinked tokilled adenovirus alone (see, e.g., Curiel, Hum. Gene Ther. (1992)3:147); ligand-linked DNA (see, e.g., Wu, J. Biol. Chem. (1989)264:16985); eukaryotic cell delivery vehicles cells (see, e.g., U.S.Pat. No. 5,814,482; PCT Publication Nos. WO 95/07994; WO 96/17072; WO95/30763; and WO 97/42338) and nucleic charge neutralization or fusionwith cell membranes. Naked DNA can also be employed. Exemplary naked DNAintroduction methods are described in PCT Publication No. WO 90/11092and U.S. Pat. No. 5,580,859. Liposomes that can act as gene deliveryvehicles are described in U.S. Pat. No. 5,422,120; PCT Publication Nos.WO 95/13796; WO 94/23697; WO 91/14445; and EP Patent No. 0524968.Additional approaches are described in Philip, Mol. Cell. Biol. (1994)14:2411, and in Woffendin, Proc. Natl. Acad. Sci. (1994) 91:1581. Thecontents of each of the foregoing are incorporated by reference hereinfor this purpose.

It is also apparent that an expression vector can be used to directexpression of any of the protein-based anti-cancer therapeutic agents(e.g., an anti-cancer antibody). For example, peptide inhibitors thatare capable of blocking (from partial to complete blocking) a cancercausing biological activity are known in the art.

In some embodiments, more than one anti-cancer therapeutic agent, suchas an antibody and a small molecule inhibitory compound, may beadministered to a subject in need of the treatment. The agents may be ofthe same type or different types from each other. At least one, at leasttwo, at least three, at least four, or at least five different agentsmay be co-administered. Generally anti-cancer agents for administrationhave complementary activities that do not adversely affect each other.Anti-cancer therapeutic agents may also be used in conjunction withother agents that serve to enhance and/or complement the effectivenessof the agents.

Treatment efficacy can be predicted as described herein for a patientprior to a treatment. Alternatively or in addition to, treatmentefficacy can be predicted and/or determined as described herein over thecourse of treatment (e.g., before, during, and after treatment). See,e.g., Example 4 and Example 5 below.

Combination Therapy

Compared to monotherapies, combinations of treatment approaches showedhigher efficacy in many studies, but the choice of remedies to becombined and designing the combination therapy regimen remainspeculative. Given that the number of possible combinations is nowextremely high, there is great need for a tool that would help to selectdrugs and combinations of remedies based on objective information abouta particular patient. Use of biomarkers as described herein fordesigning or electing a specific combination therapy establishes ascientific basis for choosing the optimal combination of preparations.

As noted above, also provided herein are methods of treating a cancer orrecommending treating a cancer using any combination of anti-cancertherapeutic agents or one or more anti-cancer therapeutic agents and oneor more additional therapies (e.g., surgery and/or radiotherapy). Theterm combination therapy, as used herein, embraces administration ofmore than one treatment (e.g., an antibody and a small molecule or anantibody and radiotherapy) in a sequential manner, that is, wherein eachtherapeutic agent is administered at a different time, as well asadministration of these therapeutic agents, or at least two of theagents or therapies, in a substantially simultaneous manner.

Sequential or substantially simultaneous administration of each agent ortherapy can be affected by any appropriate route including, but notlimited to, oral routes, intravenous routes, intramuscular, subcutaneousroutes, and direct absorption through mucous membrane tissues. Theagents or therapies can be administered by the same route or bydifferent routes. For example, a first agent (e.g., a small molecule)can be administered orally, and a second agent (e.g., an antibody) canbe administered intravenously.

As used herein, the term “sequential” means, unless otherwise specified,characterized by a regular sequence or order, e.g., if a dosage regimenincludes the administration of an antibody and a small molecule, asequential dosage regimen could include administration of the antibodybefore, simultaneously, substantially simultaneously, or afteradministration of the small molecule, but both agents will beadministered in a regular sequence or order. The term “separate” means,unless otherwise specified, to keep apart one from the other. The term“simultaneously” means, unless otherwise specified, happening or done atthe same time, i.e., the agents of the disclosure are administered atthe same time. The term “substantially simultaneously” means that theagents are administered within minutes of each other (e.g., within 10minutes of each other) and intends to embrace joint administration aswell as consecutive administration, but if the administration isconsecutive it is separated in time for only a short period (e.g., thetime it would take a medical practitioner to administer two agentsseparately). As used herein, concurrent administration and substantiallysimultaneous administration are used interchangeably. Sequentialadministration refers to temporally separated administration of theagents or therapies described herein.

Combination therapy can also embrace the administration of theanti-cancer therapeutic agent (e.g., an antibody) in further combinationwith other biologically active ingredients (e.g., a vitamin) andnon-drug therapies (e.g., surgery or radiotherapy).

It should be appreciated that any combination of anti-cancer therapeuticagents may be used in any sequence for treating a cancer. Thecombinations described herein may be selected on the basis of a numberof factors, which include but are not limited to the effectiveness ofaltering a biomarker, reducing tumor formation or tumor growth, and/oralleviating at least one symptom associated with the cancer, or theeffectiveness for mitigating the side effects of another agent of thecombination. For example, a combined therapy as provided herein mayreduce any of the side effects associated with each individual membersof the combination, for example, a side effect associated with anadministered anti-cancer agent.

EXAMPLES

In order that the systems and methods described herein may be more fullyunderstood, the following examples are set forth. The examples describedin this application are offered to illustrate the methods and systemsprovided herein and are not to be construed in any way as limiting theirscope.

Example 1: Calculating Normalized Biomarker Values Obtaining BiomarkerSets

Any number of biomarkers may be used to predict therapy efficacy using atechnique provided herein. Biomarkers used herein were obtained frompublished clinical studies shown in Table 1.

TABLE 1 Datasets used for calculating therapy scores. Number of TherapyDataset Diagnosis biomarkers aPD1 therapy Hugo et al. Melanoma 46 aCTLA4therapy Van Allen et al. Melanoma 17 IFNa therapy TCGA SKCM Melanoma 43MAGEA-3 vaccine MAGEA3 dataset Melanoma 13 GSE35640 BevacizumabBEV-bladder-GSE60331 Melanoma 11 Rituximab Based MALY-DE ICGC Follicular18 lymphoma

In the instant example, biomarkers that split the patient cohortstreated with a particular therapy by a clinical measure (e.g., overallsurvival (OS), progression-free survival (PFS), objective response rate(ORR), etc.) were used. For example, in patients treated with ananti-PD1 therapy, the PFS for patients having a high number of mutationswas 14.5 months and the PFS for patients having a low number ofmutations was 3.6 months. Thus, the number of mutations was used as aparameter for predicting therapy efficacy.

Biomarkers were defined as either positive biomarkers or negativebiomarkers based on whether the parameter value of the biomarkercorresponds to an increase or decrease in therapy response. Biomarkerswere defined as positive biomarkers if their biomarker parameter valuecorrelating to a positive therapy outcome was high. Biomarkers weredefined as negative biomarkers if their biomarker parameter valuecorrelating to a negative therapy outcome was high.

A detailed set of biomarkers for each therapy is presented in Table 2.

TABLE 2 Biomarkers obtained from published datasets. Therapy BiomarkersaPD1 Affinity of AXL B2M LOF mutation BRAF mutation therapy neontigensBRCA2 mutation Cancer gene panels Cancer gene panels CCL13 (CGPs) FM-CGP(CGPs) HSL-CGP CCL2 CCL7 CCL8 CD8+ cell density in the tumor invasivemargin CD8+ cell number CDH1 CVEGFC CX3CL1 expression CXCR2 expressionDendritic cell number EGFR expression Endothelial cells Eosinophilnumber ESRP1 expression Fibroblasts Granzyme B expression JAK1 LOFmutation JAK2 LOF mutation LDH level Lymphocyte number M1 macrophageM1/M2 macrophage MDSC % MHC-II expression number ratio MHC-II expressionMissmatch-repair MITF expression Mutational Burden (HLA-DRA) deficiencystatus Pattern of distant PD-L1 expression PD-L1 expression on PTEN lossmetastases infiltrating leukocytes Quantity of ROR2 STAT1 expression Treg cell % neoantigen peptides TAGLN TCR clonality TGFbeta level TILnumber in tumor TWIST2 VEGF level VEGFA aCTLA4 Absolute CD8+ cell numberCXCL11 expression CXCL9 expression therapy lymphocyte count CXCR3expression Dendritic cell number EOMES+CD8+ cells FOXP3+ cells numbernumber IDO expression LDH expression M1 macrophage M1/M2 macrophagenumber ratio MDSC % Mutational Burden NY-ESO-1 seropostive PTEN loss Treg cell % TCR clonality TGFbeta level TIL number in tumor VEGF levelIL-2 therapy Bone metastasis concomitant regional Leucocytes numberLNPEP expression lymphadenopathy C-reactive protein Delta32 CCR5 BCAT2expression BDNFOS level Polymorphism expression IL-10 (−1082G−>A) CAIXexpression LOC130576 CCR5 LOF polymorphism expression mutation ERCC1(codon 118) IFN-g (+874A−>T) LOC399900 ATP6V0A2 polymorphismpolymorphism expression expression Ki-67 expression Alkaline phosphataseARHGAP10 CD56+ or CD57+ level expression cells number Liver metastasisCD83+ TIDC cells cDNA FLJ37989 LDH level number expression Fibronectinlevel HLA-DQB1 GBF1 expression amount of alveolar expression componentAlbumin level clear cell FOXP3+ cells number HLA-DQA1 histologyexpression granular features MAP3K5 expression MDSC number Mediastinummetastasis MEF2A expression MTUS1 expression Neutrophil number NK cellnumber non clear cell NR1H2 expression NRAS mutations Number ofhistology metastatic sites papillary features PH-4 expression PlateletsNumber RABL2B expression RC3H2 expression rs12553173 Sedimentation rateSUPT6H expression TACC1 expression TDP1 expression TFPI expression Timefrom tumor to occurrence of metastases Transferrin level TSH level VCAM1expression VEGF level Weight loss α-antitrypsin level IFNa CAIX levelDelta32 CCR5 Leucocytes count LNPEP expression therapy PolymorphismERCC1 (codon 118) GBF1 expression Bone metastasis Breslow thicknesspolymorphism IL-6 expression CCR5 LOF mutation LOC130576 CD4+ cellsnumber level expression Hepatic RIG-1 IL-1β expression LOC399900 BDNFOSexpression level expression expression Interval from initial ARHGAP10BCAT2 expression CD8+ CD57+ cells diagnosis to expression numbertreatment Liver metastasis CD83+ TIDC cells cDNA FLJ37989 fis Ki-67expression number expression HLA-Cw06 allele IL-1α expression HLA-DQB1ATP6V0A2 level expression expression Alkaline collagen IV level HLA-DQA1IL-10 (−1082G−>A) phosphatase level expression polymorphism IFN-g(+874A−>T) MAP3K5 expression Mediastinum metastasis MEF2A expressionpolymorphism MIP-1α expression MIP-1β expression MTAP gene expressionMTUS1 expression level level Neutrophil count NR1H2 expression Number ofmetastatic Osteopontin level sites Performance status PH-4 expressionPlatelets Number RABL2B expression RC3H2 expression Sedimentation rateSerum calcium level Serum hemoglobin level STAT1 gene SUPT6H expressionTACC1 expression TDP1 expression expression TFPI expression Time fromtumor to TNF-α expression level TRAIL level occurrence of metastasesUlceration of VCAM1 expression VEGF level VEGFR2 level primaryAnti-cancer Cancer-Testis CD16+CD56+CD69+ CD4+CD45RO+ cell CD4+CTLA-4+ Tvaccine Antigens' Genes lymphocytes number cell number therapyexpression number CD4+PD-1+ T cell C-reactive protein ECOG performanceEGF level number level score I/II high-grade or III IFN-gamma-inducedIgM for Blood Group A IL-6 level T1/2/3a low-grade tumor cell apoptosistrisaccharide level disease intermediate risk Intratumoral versus LDHlevel Lin-CD14+HLA-DR-/lo lymphocyte number peritumoral T cell MDSClevel density lymphocytes in M1/M2 macrophage MDSC number MeanCorpuscular PBMC % ratio Hemoglobin Concentration (MCHC) Number ofPatient's age Predictive gene PTEN loss CD27−CD45RA+ and signature inMAGE A3 CD27−CD45RA− antigen-specific cancer and immunotherapyCD27+CD45RA− T-cells Serum amyloid A Serum S100B Syndecan-4 mRNA T regcell % level concentration expression level TGFbeta level Toll-likereceptor 4 WT1 expression gene polymorphism Anti- Acneiform rashAdrenomedullin angiopoietin-2 Bioactive Peptide angiogenic Repeatexpression levels Induced Signaling therapy Polymorphism Pathway CD133expression CDC16 level Child-Pugh class CXCL10 plasma level CXCR1rs2234671 CXCR2 C785T CXCR2 rs2230054 ECOG Performance G > C T > CStatus EGF A-61G EGF rs444903 A > G EGFR expression levels EGFRrs2227983 G > A Endothelin-1 Expression of CD31 Expression of PDGFR- HBVstatus expression levels beta HGF plasma level History of alcohol ICAM1T469C IFN-α2 plasma level intake IGF-1 rs6220 A > G IL-12 plasma levelIL-16 plasma level IL-2Rα plasma level IL-3 plasma level IL-6 plasmalevel IL-8 251 T > A IL-8 plasma level Lck and Fyn Liver metastasisM-CSF plasma level mucinous histology tyrosine kinases in initiation ofTCR Activation pathway activation NO2-dependent IL Number of restingNumber of total PIGF plasma level 12 Pathway circulating circulatingendothelial activation in NK endothelial cells cells cells portal veinrs12505758 in rs2286455 rs3130 thrombosis VEGFR2 rs699946 in VEGFASDF-1α plasma level Sex sVEGFR1 T Cell Receptor T Helper Cell SurfaceTRAIL plasma level VEGF -1154 A > G Signaling Pathway Moleculesexpression activation VEGF -1498 C > T VEGF C936T VEGF G-634C VEGF-1154G/A VEGF-2578 C/A VEGFR1 rs9582036 VEGFR-2 rs2305948 WNK1-rs11064560 C >T Rituximab BCL2 expression BCL6 expression Beclin-1 expression C1qAGene level Polymorphism Carbohydrate CD163-positive CD20 expression CD37expression antigen-125 level macrophages level CD5 expression CXCR4expression Cytotoxic T FcγRIIIa 158H/H level level lymphocyte-associatedgenotypes Granzyme B expression level Galectin-1 HIP1R mRNA level IL-12level IL-1RA level expression Ki-67 expression MARCO expression Mastcell number 1 miR-155 expression MYC expression Number of p21 proteinexpression sLR11 level macrophages SMAD1 expression STAT3 T cells TAMnumber mRNA level TIM3 expression

Calculating Normalized Biomarker Values

To analyze different parameters (e.g., T cell number, MHC proteinexpression, BRAF mutation, etc.) or parameters for which biomarker“threshold” values could not be clearly interpreted, methods tonormalize biomarker values were developed. Normalized biomarker valuesare herein described in terms of “high,” “medium” and “low,” wheremathematically high values correspond to 1 and mathematically low valuescorrespond to −1.

Biomarkers with digital properties, such as certain mutations (e.g.,BRAFV600E), were normalized using a binary system, where presence of abiomarker corresponded to 1, and absence of a biomarker corresponded to0. Biomarkers associated with protein expression such as thosedetermined from tissue staining experiments, were assigned theircorresponding gene expression (e.g., target protein assigned target mRNAexpression level). Biomarkers associated with cellular composition inthe tumor microenviroment were recalculated with bioinformatics celldeconvolution packages based on RNAseq data (e.g., MCPcounter,CIBERSORT).

Normalized biomarker scores were calculated for a large patient cohortin which patients were diagnosed based on their tumor biopsy. Data wasobtained from publicly available databases of human cancer biopsies, anddata was normalized for a particular patient using one of the belowformulas according to the distribution of biomarker values calculatedfor the large patient cohort.

Normalized parameter values in terms of “high” and “low” were calculatedbased on the Z-score of the parameter value using predefinedmathematical functions where the normalized parameter value ranges from−1 to 1 depending on Z-score. Mean and standard deviation were takenfrom a previously calculated distribution of parameter values for thelarge patient cohort to which the patient belonged.

The function was set so that a zero value of the parameter fell in themiddle of the distribution, and the highest values were assigned toparameters at the extreme upper end of the distribution.

Unit step function a):

$\left\{ \begin{matrix}{1,\ {{{{if}\mspace{14mu} Z} - \ {{score}\mspace{14mu} {value}}}\  > C^{+ {cutoff}}}} \\{0,{{{if}\mspace{14mu} C^{- {cutoff}}} < {Z - \ {{score}\mspace{14mu} {value}}}\  < C^{+ {cutoff}}}} \\{{- 1},\ {{{{if}\mspace{14mu} Z} - \ {{score}\mspace{14mu} {value}}}\  < C^{- {cutoff}}}}\end{matrix} \right.\quad$

Where C^(+cutoff)=normalized threshold value representing a “high”parameter value, and C^(−cutoff)=normalized threshold value representinga “low” parameter value.

Or:

Flattened unit step function b):

$\left\{ \begin{matrix}{\frac{\left| {Z - {{score}\mspace{14mu} {value}}} \right|^{a}}{\left| {Z - {{score}\mspace{14mu} {value}}} \middle| {}_{a}{+ \left| C^{+ {cutoff}} \right|^{a}} \right.}\ ,\ {{{{if}\mspace{14mu} Z} - \ {{score}\mspace{14mu} {value}}}\  \geq 0}} \\{\frac{\left| {Z - {{score}\mspace{14mu} {value}}} \right|^{a}}{\left| {Z - {{score}\mspace{14mu} {value}}} \middle| {}_{a}{+ \left| C^{- {cutoff}} \right|^{a}} \right.}\ ,\ {{{{if}\mspace{14mu} Z} - \ {{score}\mspace{14mu} {value}}} < 0}}\end{matrix} \right.\quad$

Where a>1=parameter defining the slope of the unit step function. Witha>∞ function b) transforms to function a).

Threshold value C^(+cutoff) (C^(−cutoff)) was equal to 1 (−1),indicating that 15% of patients had a high biomarker value, and 15% ofpatients had a low biomarker value. Different cut-offs may be useddepending on the biomarkers involved in the calculation.

After normalization, each biomarker was transformed to the same rangescale. Thus, a value equal to 1 represents a “high” parameter value, anda value equal to −1 represents a “low” parameter value. Parameter valuesequal or close to 0 reflect median parameter values according to thedistribution. A graphical representation of biomarker value distributionfor a large patient cohort is shown in FIG. 3.

Defining Biomarker Significance

Biomarkers were assigned weights indicative of their predictivesignificance based on whether the biomarker was obtained from a large orsmall patient cohort. Biomarkers obtained from studies using largepatient cohorts may have higher predictive significance, and thereforethese biomarkers were assigned an initial numeric weight of 3.Biomarkers obtained from studies using small patient cohorts may havelower predictive significance, and therefore these biomarkers wereassigned an initial numeric weight of 1.

Biomarkers were assigned weights indicative of their predictivesignificance based on the role of the biomarker with respect to atherapy. For example, when analyzing biomarkers for treatment with ananti-PD1 therapy, PDL expression was a significant biomarker that wasassigned a higher numeric weight than a less significant biomarker suchas gender.

Biomarker significance in terms of “weight” was defined by expertassessment or clinical studies where the biomarker was identified.Significance or weight was based on clinical measures (e.g., patientoutcome) that split two cohorts of patients divided by biomarker value.If the difference among clinical outcomes for a biomarker was large(p-value<0.01), it was assigned a high weight. If the clinicaldifference for a biomarker was minimal (0.01<p-value<0.05), thebiomarker weight was assigned a low weight.

Alternatively or in addition to the foregoing, biomarker significancewas calculated for a biomarker within a set of biomarkers using machinelearning algorithms. This approach involved extensive “training” ofdatasets. A set of biomarkers obtained from literature was testedmathematically to improve weights manually assigned to biomarkers. Thealgorithm provided a list of significant biomarkers and insignificantbiomarkers. Insignificant biomarkers were excluded from the initial setwithout loss of prediction accuracy.

Example 2: Therapy Scores Calculated from Biomarkers

Therapy scores were calculated for five patients using a sum ofnormalized biomarker values multiplied by their “weight”. Patient 1 andPatient 2 had more positive biomarkers, and thus had higher therapyscores (FIG. 4). Patient 4 had similar numbers of positive and negativebiomarkers and Patient 5 had biomarkers with neutral values, and thusthese patients had therapy scores of zero (FIG. 4). Patient 3 had agreater number of negative biomarkers, and thus has a negative therapyscore (FIG. 4).

Example 3: Therapy Scores Predicted Treatment Response

Therapy scores for different therapies were calculated for anon-responsive patient (Patient 1) and a responsive patient (Patient 2)with respect to their response to the anti-PD1 therapy Pembrolizumab.Based on the calculated therapy scores, Patient 1 was likelynon-responsive to other treatments including anti-CTLA4 therapy, IL-2therapy, vaccine therapy, and Bevacizumab (FIG. 5). However, Patient 1'stherapy score predicted a likely response to IFN-α therapy (FIG. 5).Patient 2's therapy scores predicted a likely response to eachtreatment. These results demonstrated that therapy scores predicted botha response and a non-response to a therapy.

Therapy scores were calculated as described herein for an anti-PD1therapy dataset and an anti-CTLA4 dataset. Patients treated with ananti-PD1 therapy having higher therapy scores calculated as a sum ofpositive and negative biomarkers were more likely to respond to therapy,and patients with negative therapy scores were unlikely to respond totherapy (FIG. 7A). Similar results were obtained for patients treatedwith an anti-CTLA4 therapy (FIG. 7B).

Predictive accuracy was improved by using a prediction cut-off. Forexample, analysis of the anti-PD1 therapy dataset showed that theprediction rate was 73% when the non-response cut-off was lower thanzero and 88% when the non-response cut-off was lower than −1 (FIG. 7C).Similarly, the prediction rate was 80% when the response cut-off washigher than zero and improved to 91% when the response cut-off washigher than 1 (FIG. 7C). Therapy response rate predictions based oncertain cut-offs for various therapies are shown in Table 3.

TABLE 3 Therapy response rate prediction. Non-response Pre- ResponsePre- cut-off diction cut-off diction Therapy (lower than) rate (higherthan) rate aPD1 therapy 0 73% 0 80% aCTLA4 therapy −1 77% — — IFNatherapy 0 100%  0 70% MAGEA-3 vaccine −2 94% 0 50% Bevacizumab −1 80% 180% Rituximab Based — — 0 100% 

Example 4: Biomarker Weight Optimization Improved Therapy ScorePrediction Accuracy

Using an anti-PD1 therapy dataset obtained from Hugo et al., theprediction accuracy of therapy scores calculated with biomarker weightoptimization were compared to those calculated without biomarker weightoptimization. Therapy scores calculated without biomarker weightoptimization accurately predicted therapy response for 73% of patientsin the study (FIG. 8A). Calculating therapy scores with biomarker weightoptimization improved the prediction rate to 85%. Biomarker weightoptimization included calculating feature importance using random forestregression, in which abundant biomarkers were assigned higher importancefor predicting a therapy response (FIG. 8C). Biomarker weights wererecalculated with a logistic regression model to obtain the bestprediction of therapy response (FIG. 8D).

Example 5: Calculated Therapy Scores for Different Therapies

Different combinations of biomarkers were used for calculating therapyscores for different therapies. Normalized biomarker values for eachpatient treated with anti-PD1 therapy (Table 4-5), aCTLA4 therapy (Table6-7), IFNα therapy (Table 8), anti-cancer vaccine therapy (Table 9-10),and anti-angiogenic therapy (Table 11) were calculated.

TABLE 4 Set of normalized biomarker values for each patient having anegative therapy score treated with aPD1 therapy. ID NO: 87 88 03 29.083 95 02 28.0 90 82 96 79 92 84 89 Response PD PD PD PR PD PD PD PR PDPD PD PD CR MiR-BART9 expression 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 Cancer gene panels (CGPs) 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 FM-CGP Cancer gene panels (CGPs) 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 HSL-CGP BRAFmutation 0.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 0.0 3.0 0.0 3.0STAT1 expression 0.0 0.0 −0.2 −0.2 −1.6 0.0 −1.0 −1.0 0.0 −0.4 0.0 0.20.0 −0.7 0.2 Granzyme B expression −0.2 0.0 0.0 0.0 −0.6 0.0 0.0 0.0 0.0−0.9 0.0 0.0 0.0 0.0 0.4 Hugogene/AXL −0.5 −0.4 −0.2 −0.2 0.0 0.0 0.00.0 −0.5 0.4 0.0 0.0 0.0 0.4 −0.4 Hugogene/ROR2 −0.5 −0.3 −0.5 −0.5 0.0−0.4 −0.4 −0.4 −0.1 0.0 −0.1 −0.5 0.0 −0.4 0.0 Hugogene/TAGLN −0.2 0.00.0 0.0 0.3 −0.5 −0.5 −0.5 −0.2 0.2 −0.4 −0.5 0.0 0.1 −0.5Hugogene/TWIST2 −0.5 −0.2 0.0 0.0 −0.5 −0.4 −0.1 −0.1 −0.5 0.1 −0.3 −0.5−0.5 0.0 0.0 Hugogene/CDH1 −2.8 −2.9 −2.4 −2.4 0.3 0.0 0.0 0.0 −2.9 1.6−1.6 0.5 0.0 −2.5 −2.8 Hugogene/CCL2 0.0 −0.2 −0.4 −0.4 0.4 −0.3 −0.1−0.1 −0.5 0.3 0.0 −0.3 −0.3 0.0 −0.5 Hugogene/CCL7 −0.3 0.0 0.0 0.0 0.00.0 −0.5 −0.5 −0.5 0.1 −0.5 0.0 −0.5 0.0 −0.5 Hugogene/CCL8 0.0 0.0 0.00.0 0.4 −0.3 −0.3 −0.3 −0.5 0.2 −0.5 0.0 −0.4 0.0 −0.4 Hugogene/CCL130.1 0.0 0.0 0.0 0.0 0.0 0.1 0.1 −0.5 0.0 0.0 −0.5 −0.5 0.3 −0.1Hugogene/CVEGFC 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 Hugogene/VEGFA −0.9 0.0 0.0 0.0 0.0 0.0 0.1 0.1 −1.0 0.0 0.0 0.0−0.2 −0.1 −1.0 EGFR expression 0.3 0.0 0.5 0.5 0.0 0.0 0.0 0.0 0.5 −0.20.0 0.2 0.0 0.0 0.0 JAK1 LOF mutation 0.0 0.0 0.0 0.0 0.0 0.0 −1.5 −1.50.0 0.0 0.0 0.0 0.0 0.0 0.0 JAK1 LOF mutation 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 B2M LOF mutation 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 LDH level −0.9 0.5 0.3 0.3 0.00.0 0.0 0.0 −0.9 0.0 0.8 0.0 −0.6 −0.8 −0.8Pattern_of_distant_metastases 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 Lymphocyte number −1.3 −1.0 0.0 0.0 −2.3 0.0 0.0 0.0−0.4 −2.4 −0.2 0.0 −0.1 −1.9 0.0 Eosinophil number 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Missmatch-repair deficiency 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 status PD-L1expression −0.7 −0.6 −0.5 −0.5 −0.3 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.0 −0.10.1 TCR clonality −1.6 −0.6 0.0 0.0 −1.6 1.8 0.0 0.0 1.6 −1.6 0.0 −0.53.0 2.4 3.0 Quantity of neoantigen −0.2 −0.6 −0.2 −0.2 −0.2 −0.7 0.0 0.00.4 −0.1 −0.1 1.1 0.0 0.0 0.0 peptides Affinity of neontigens 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 CXCR2 expression−0.5 0.0 −0.4 −0.4 −0.5 −0.3 0.0 0.0 0.0 0.0 −0.5 0.0 −0.5 0.0 0.0 ESRP1expression 1.0 0.0 1.0 1.0 0.3 0.0 0.5 0.5 1.0 −0.1 0.0 0.0 0.5 1.0 1.0MITF expression 0.8 0.5 0.9 0.9 −0.2 0.0 0.0 0.0 1.0 0.0 0.0 0.0 −0.20.0 0.6 Mutational Burden −0.1 −0.4 −0.1 −0.1 −0.1 −0.7 0.0 0.0 0.5 −0.1−0.1 0.5 0.0 0.0 0.0 BRCA2 mutation 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 3.0 0.0 0.0 0.0 CD8+ cell density in the −2.4 −2.2 0.0 0.0 −2.10.0 0.0 0.0 −0.1 −2.3 0.0 0.1 −0.7 −0.1 0.4 tumor invasive margin MHC-IIexpression −2.6 0.0 0.0 0.0 0.0 0.0 −0.7 −0.7 0.0 −0.7 0.0 0.1 −0.1 −0.22.2 EGFR expression 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 CX3CL1 expression 0.3 −0.2 0.0 0.0 0.0 0.0 0.7 0.7 0.5 −1.0 0.7−0.8 1.0 1.0 0.9 PD-L1 expression on −2.2 −1.7 −1.5 −1.5 −0.8 0.0 0.00.0 3.0 −0.1 0.0 0.0 0.0 −0.3 0.1 infiltrating leukocytes VEGF level−2.2 0.0 −0.8 −0.8 0.1 0.0 0.0 0.0 −3.0 0.0 0.0 −0.1 −1.8 0.0 −3.0TGFbeta level −0.8 0.0 −3.0 −3.0 0.0 −2.9 0.0 0.0 −2.6 0.4 0.0 −1.4 0.00.0 −1.1 M1/M2 macrophage ratio −0.3 −0.1 −0.1 −0.1 −0.3 0.0 0.0 0.0 0.0−0.3 0.0 0.0 −0.3 −0.1 0.0 T reg cell % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 MDSC % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 TIL number in tumor 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 CD8+ cell number 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 M1 macrophage number −2.4 −0.1 −0.2−0.2 −2.4 0.3 0.0 0.0 0.0 −2.4 0.5 0.0 −2.4 0.0 1.4 Dendritic cellnumber 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0Mutational Burden 0.0 −0.1 0.0 0.0 0.0 −0.2 0.0 0.0 0.5 0.0 0.0 0.5 0.00.0 0.0 TCR clonality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 PTEN loss 0.0 0.1 0.0 0.0 −0.3 0.0 −2.4 −2.4 0.0 0.8 0.0 1.51.6 −0.5 −0.2 Fibroblasts −2.9 −2.0 −3.0 −3.0 0.0 −3.0 −2.4 −2.4 −3.00.3 −1.5 −2.9 0.0 0.0 −2.9 Endothelial cells −1.6 −0.2 −2.0 −2.0 0.0−3.0 −0.1 −0.1 −3.0 0.0 −2.7 −3.0 −2.2 0.4 −0.1 Therapy Score −26.2 −9.9−9.6 −9.6 −9.0 −7.5 −5.6 −5.6 −5.2 −5.0 −3.5 −3.4 −2.4 −2.0 −0.9Abbreviations; PR—partial response, SD—stable disease, CR—completeresponse, and CPD—clinical progressive disease.

TABLE 5 Set of normalized biomarker values for each patient having apositive therapy score treated with aPD1 therapy. ID NO: 285 300 297 301291 293 304 286 305 280 294 281 306 299 298 Response CR PR PD PD PR PRPR CR PR PR PD PR PR CR CR MiR-BART9 expression 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Cancer gene panels (CGPs) 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 FM-CGP Cancer genepanels (CGPs) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 HSL-CGP BRAF mutation 3.0 0.0 0.0 0.0 0.0 0.0 3.0 3.0 0.0 0.0 0.00.0 3.0 0.0 0.0 STAT1 expression −0.1 −2.5 0.0 0.0 0.0 −2.8 0.2 0.0 −1.10.3 1.1 0.0 1.1 0.3 2.0 Granzyme B expression −0.1 −0.5 0.0 0.0 0.0 −0.60.0 0.0 0.0 −0.3 0.8 0.0 1.0 0.0 0.0 Hugogene/AXL 0.0 0.4 0.0 0.0 0.00.4 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 −0.1 Hugogene/ROR2 0.0 0.0 0.2 0.00.2 0.0 0.0 0.0 0.2 0.3 0.0 0.2 0.0 0.0 0.0 Hugogene/TAGLN 0.0 0.0 0.00.0 0.1 0.0 0.0 0.0 0.4 0.1 0.1 0.0 −0.2 0.3 0.0 Hugogene/TWIST2 −0.2−0.4 −0.5 0.0 0.0 0.0 0.0 −0.5 0.1 0.0 0.0 0.1 0.0 0.0 −0.3Hugogene/CDH1 1.3 0.8 −2.9 −0.4 0.2 0.0 1.2 0.0 −2.1 1.4 −0.9 0.9 0.02.1 0.4 Hugogene/CCL2 −0.4 −0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 −0.3 0.0−0.1 0.0 0.0 Hugogene/CCL7 −0.4 −0.5 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.00.1 0.1 0.1 0.0 Hugogene/CCL8 −0.4 0.2 0.0 −0.4 −0.1 0.3 0.0 −0.3 0.30.0 −0.5 0.0 0.0 0.0 0.0 Hugogene/CCL13 −0.5 0.1 0.0 0.0 0.2 0.3 0.0−0.5 0.3 0.2 −0.2 0.2 0.2 0.3 0.0 Hugogene/CVEGFC 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Hugogene/VEGFA 0.0 0.0 0.9 0.00.6 1.0 0.0 0.8 0.9 0.0 0.7 0.4 0.4 0.2 0.2 EGFR expression 0.0 0.5 0.00.0 0.0 −0.1 −0.1 0.1 −0.2 0.5 0.0 0.0 0.0 0.0 0.0 JAK1 LOF mutation 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 −1.5 0.0 0.0 0.0 JAK1 LOFmutation 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 B2MLOF mutation 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0LDH level 0.3 0.0 1.0 0.1 0.1 1.0 0.0 0.0 −0.3 0.1 0.0 0.0 0.2 −0.4 −0.6Pattern_of_distant_metastases 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 Lymphocyte number −1.3 −0.1 0.0 0.0 0.0 −0.1 −0.10.0 0.0 −0.8 2.9 −0.1 3.0 −0.2 0.0 Eosinophil number 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Missmatch-repair deficiency 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.5 0.0 1.5 1.5 status PD-L1expression 0.0 2.8 0.0 0.0 −0.1 −0.1 0.6 0.0 0.0 0.2 2.4 0.0 2.9 0.6 1.2TCR clonality 0.0 −1.2 0.0 −0.9 0.1 0.0 −0.1 0.0 0.0 0.0 0.0 0.0 −0.60.0 2.6 Quantity of neoantigen 0.0 0.0 −0.5 −0.2 0.0 0.0 −0.1 0.0 0.02.4 0.0 3.0 −0.7 2.9 2.9 peptides Affinity of neontigens 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 CXCR2 expression 0.0 −0.50.0 0.0 0.0 0.0 0.0 0.0 0.0 −0.5 0.0 −0.5 0.0 0.0 0.0 ESRP1 expression0.0 −1.0 0.0 0.0 −0.1 0.4 −0.8 0.0 0.0 0.1 0.0 0.0 0.8 0.0 0.0 MITFexpression 0.0 1.0 0.0 0.0 0.0 0.3 −0.5 0.0 0.0 0.0 −0.1 0.0 0.6 −0.7−0.5 Mutational Burden 0.0 −0.1 −0.3 −0.2 0.0 0.0 −0.1 0.0 0.0 2.8 0.03.0 −0.5 2.7 2.7 BRCA2 mutation 0.0 3.0 0.0 0.0 0.0 3.0 0.0 0.0 0.0 0.00.0 3.0 0.0 3.0 3.0 CD8+ cell density in the 0.0 −1.4 0.7 1.0 0.0 0.00.0 0.1 0.0 0.0 3.0 −0.1 1.7 −0.1 0.0 tumor invasive margin MHC-IIexpression 0.0 −0.1 0.0 0.9 0.0 0.0 0.0 0.0 −0.1 0.0 2.5 0.0 2.4 0.0 0.9EGFR expression 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 CX3CL1 expression 0.2 0.9 0.0 1.0 1.0 0.0 −0.5 −0.2 −0.7 0.0 0.0 0.00.0 −0.6 −0.4 PD-L1 expression on 0.0 2.8 0.0 0.0 −0.2 −0.4 0.6 0.0 0.00.2 2.4 0.0 2.9 0.6 1.2 infiltrating leukocytes VEGF level 0.0 0.0 2.80.0 2.1 3.0 0.0 2.6 2.9 0.0 1.3 2.5 0.0 0.9 0.2 TGFbeta level −0.6 0.12.1 0.0 0.2 0.0 0.0 0.0 2.9 0.0 0.7 0.0 −0.1 0.0 0.0 M1/M2 macrophageratio 0.0 −0.3 0.0 0.5 0.0 0.0 0.9 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 T regcell % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MDSC% 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TIL numberin tumor 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.9 0.0 0.0CD8+ cell number 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 M1 macrophage number 0.0 −2.4 0.0 3.0 0.0 0.0 3.0 2.8 0.0 0.3 0.00.8 −0.1 2.9 3.0 Dendritic cell number 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 Mutational Burden 0.0 0.0 −0.1 −0.1 0.0 0.00.0 0.0 0.0 2.8 0.0 3.0 −0.2 2.7 2.7 TCR clonality 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 PTEN loss 0.2 0.0 −0.2 0.0 −0.7−2.3 0.0 0.0 0.0 0.0 0.0 0.0 0.6 2.8 2.5 Fibroblasts −0.2 0.0 0.1 0.00.6 0.0 0.0 −0.1 2.9 2.2 0.9 2.1 0.0 0.0 0.0 Endothelial cells −0.3 0.00.0 0.0 1.2 2.6 −1.2 −0.2 2.9 0.2 0.9 0.0 0.0 0.6 0.0 Therapy Score 0.81.4 3.5 4.4 5.3 5.6 6.1 7.9 10.2 12.4 17.6 18.5 20.0 22.6 25.1Abbreviations; PR—partial response, SD—stable disease, CR—completeresponse, and CPD—clinical progressive disease.

TABLE 6 Set of normalized biomarker values for each patient having anegative therapy score treated with aCTLA4 therapy. PD PD PD PD PD PD SDPD PD PD PD ID NO: 35 38 31 29 16 22 10 28 32 21 34 CXCL9 −0.9 −0.9 0.0−0.8 −0.4 −0.9 −0.3 −0.9 −0.9 0.0 −0.9 expression CXCL11 −0.8 −0.7 0.01.0 −0.5 −0.5 −0.7 −0.2 −0.8 0.0 −0.8 expression CXCR3 0.0 −0.4 −0.4−0.6 0.0 −0.7 0.8 −0.7 −0.7 0.0 −0.5 expression VEGF level −3.0 0.0 −2.80.0 0.0 0.0 −2.4 0.0 0.8 −1.2 2.2 MDSC % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 FOXP3+ cells 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0number Absolute 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 lymphocytecount IDO −2.6 −1.9 −1.2 −2.3 −2.0 −0.7 −0.5 −0.6 −2.6 0.0 −2.5expression NY-ESO-1 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0seropostive EOMES+CD8+ −0.3 −0.2 −0.2 −0.3 0.0 −0.3 0.0 −0.1 −0.3 0.0−0.3 cells number LDH −0.2 0.0 0.0 0.0 0.0 0.1 0.2 0.0 0.0 −0.1 0.0expression VEGF level 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0TGFbeta level −0.9 0.0 −0.3 −1.0 −0.8 0.0 −0.8 0.1 0.9 −0.6 0.2 M1/M2macrophage 0.0 −0.1 −0.1 −0.1 −0.1 0.0 −0.1 −0.2 −0.2 −0.1 −0.2 ratio Treg cell % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MDSC % 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TIL number in −2.3 −2.6 −0.3 −2.6−1.4 −2.9 0.0 −1.9 −2.4 0.0 −2.8 tumor CD8+ cell −0.7 −0.7 −0.3 −0.1 0.0−0.7 −0.2 −0.7 −0.5 −0.6 0.0 number M1 macrophage 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 number Dendritic cell 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 number Mutational 2.1 0.0 −0.1 −0.1 0.0 0.0 0.0 −0.10.0 0.0 0.0 Burden TCR clonality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 PTEN loss 1.0 −0.4 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 Therapy Score−8.6 −7.9 −5.1 −6.8 −5.3 −6.6 −3.3 −5.2 −5.7 −2.6 −5.7 PD PD CR SD PD PDPR PR PD SD ID NO: 30 36 4 13 26 41 5 6 37 11 CXCL9 0.6 0.0 −0.9 0.1 0.00.0 0.0 0.2 0.0 0.0 expression CXCL11 0.0 0.0 0.0 0.0 −0.1 0.0 0.0 0.1−0.3 0.0 expression CXCR3 −0.2 0.0 −0.3 0.0 0.0 0.0 0.0 −0.3 0.0 −0.6expression VEGF level −0.2 −1.1 0.0 −0.3 0.0 0.0 0.0 0.0 0.3 0.0 MDSC %0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 FOXP3+ cells 0.0 0.0 0.0 0.0 0.10.0 0.0 0.0 0.0 0.0 number Absolute 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 lymphocyte count IDO 0.0 −0.1 −0.1 0.0 0.0 −0.5 −1.1 0.0 0.0 0.0expression NY-ESO-1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 seropostiveEOMES+CD8+ 0.0 −0.2 0.0 0.0 0.0 0.0 0.0 0.0 −0.2 0.0 cells number LDH−2.2 0.0 −0.1 −0.8 −0.2 0.0 0.0 −0.6 0.0 0.0 expression VEGF level 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TGFbeta level 0.0 0.0 0.0 0.0 −0.10.1 0.0 0.5 0.2 1.0 M1/M2 macrophage 0.0 −0.1 −0.1 0.0 −0.1 0.0 0.0 0.20.0 0.0 ratio T reg cell % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MDSC% 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TIL number in 0.0 −0.1 −0.10.0 0.0 0.0 −0.5 −0.1 0.0 −0.5 tumor CD8+ cell −0.6 −0.7 0.0 0.0 −0.30.0 0.6 0.0 0.0 0.7 number M1 macrophage 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 number Dendritic cell 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0number Mutational 0.0 0.0 0.0 0.0 −0.1 −0.1 0.0 0.0 0.0 0.0 Burden TCRclonality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 PTEN loss 0.0 0.0 0.0−0.2 0.0 0.0 0.0 −0.1 0.0 −0.7 Therapy Score −2.6 −2.2 −1.6 −1.3 −0.8−0.6 −0.9 0.0 −0.1 −0.2 Abbreviations; PR—partial response, SD—stabledisease, CR—complete response, and CPD—clinical progressive disease.

TABLE 7 Set of normalized biomarker values for each patient having apositive therapy score treated with aCTLA4 therapy. PD PD SD PD PD CR PRPD PD PD PD PD PD PD SD PR PD SD PR 19 14 24 33 3 8 17 20 25 27 40 18 3912 7 23 15 9 CXCL9 0.4 0.0 0.3 0.7 0.0 0.0 0.0 0.0 −0.8 0.7 1.0 0.9 0.30.9 0.7 0.0 1.0 0.0 expression CXCL11 0.0 −0.3 0.0 0.8 −0.8 0.0 0.7 0.00.0 0.1 0.7 0.5 0.0 0.8 0.3 0.0 0.9 1.0 expression CXCR3 0.0 −0.2 0.80.9 0.0 0.0 0.9 −0.3 0.0 0.4 1.0 0.1 0.9 1.0 0.8 0.0 0.8 0.0 expressionVEGF level 0.1 0.0 0.0 −0.6 −0.4 0.4 0.0 2.1 1.2 0.0 0.0 0.0 1.4 0.0 0.01.5 0.0 3.0 MDSC % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 FOXP3+ cells 0.0 0.0 1.8 1.7 0.0 0.0 3.0 0.0 0.0 0.02.9 1.2 0.0 2.0 1.1 0.0 0.0 0.0 number Absolute 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 lymphocyte count IDO 0.00.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.2 0.9 0.9 0.0 1.0 0.9expression NY-ESO-1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 1.0 0.00.0 0.0 0.3 0.0 0.0 seropostive EOMES+CD8+ 0.0 −0.1 0.0 0.8 0.0 0.0 0.20.0 0.0 1.0 1.0 0.0 0.4 1.0 0.6 0.0 1.0 0.0 cells number LDH 0.0 0.0 0.00.1 0.0 0.0 0.3 0.0 0.2 −2.8 0.1 0.0 0.0 0.4 −0.3 0.0 −0.2 3.0expression VEGF level 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 TGFbeta level −0.1 0.0 −0.2 −0.9 0.0 0.8 0.0 0.80.0 0.5 −0.9 0.0 0.3 −0.2 0.0 0.9 0.9 1.0 M1/M2 macrophage 0.0 −0.1 0.00.0 −0.1 0.0 0.0 −0.2 0.0 0.9 0.4 0.7 0.9 1.0 0.0 0.9 1.0 −0.2 ratio Treg cell % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 MDSC % 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 TIL number in 0.0 −0.1 1.3 0.4 2.9 0.0 2.9 0.0 0.00.0 2.8 0.0 0.7 2.9 1.8 0.0 2.6 0.3 tumor CD8+ cell 0.1 0.1 0.0 0.0 0.00.0 −0.3 0.0 1.0 0.9 0.6 0.0 1.0 1.0 0.9 0.0 1.0 0.0 number M1macrophage 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 number Dendritic cell 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 number Mutational 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 −0.1 0.1 0.0 0.0 −0.1 0.2 3.0 3.0 2.8 0.0 Burden TCRclonality 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 PTEN loss −0.3 0.8 0.0 0.0 −0.4 0.0 0.0 −0.7 0.0 0.0 0.0 0.90.0 0.0 0.0 0.0 −0.2 1.0 Therapy Score 0.2 0.1 3.9 4.0 1.3 1.2 7.7 1.92.6 3.7 10.5 5.3 6.1 11.9 9.7 6.8 12.6 10.0 Abbreviations; PR—partialresponse, SD—stable disease, CR—complete response, and CPD—clinicalprogressive disease.

TABLE 8 Set of normalized biomarker values for each patient treated withIFNα therapy. FR- FS- FS- D3- FW- FS- YG- EB- FW- TCGA A7U8 A1ZS A4F0A2JP A3TV A1ZW AA3O A6QY A3R5 Response PR SD SD PR SD PR CR CR CR ID:847 4526 2367 1812 411 1505 1154 382 1124 ID NO: 10 16 19 9 13 18 11 4 6Delta32 CCR5 0 0 0 0 0 0 0 0 0 Polymorphism CCR5 LOF 0 0 0 0 0 0 0 0 0mutation IFN-g 0 0 0 0 0 0 0 0 0 (+874A− > T) polymorphism IL-10 0 0 0 00 0 0 0 0 (−1082G− > A) polymorphism ERCC1 0 0 0 0 0 0 0 0 0 (codon 118)polymorphism VCAM1 −0.8 −0 −0.8 1 −0 0 −0 −0.5 0 expression PlateletsNumber 0 0 0 0 0 0 0 0 0 Alkaline 0 0 0 0 0 0 0 0 0 phosphatase levelSedimentation 0 0 0 0 0 0 0 0 0 rate Weight loss 0 0 0 0 0 0 0 0 0 Timefrom tumor 0 0 0 0 0 0 0 0 0 to occurrence of metastases Number of 0 0 00 0 0 0 0 0 metastatic sites Bone metastasis 0 0 0 0 0 0 0 0 0 Livermetastasis 0 0 0 0 0 0 0 0 0 Mediastinum 0 0 0 0 0 0 0 0 0 metastasisGBF1 expression 0.5 −0.5 −0.4 0.5 −0 0.3 −0.2 −0.1 −0.3 LNPEP −0.4 0.50.5 −0 0.5 −0 0 0 −0 expression MAP3K5 −0.4 0.5 0 −0 −0 −0.2 0 0 −0expression cDNA FLJ37989 0 0 0 0 0 0 0 0 0 fis expression RABL2B 0 0 −00.3 −0.3 0.4 −0.5 −0.1 0 expression MEF2A −0 0.5 0.2 −0 0 0 −0 0 0.2expression LOC399900 0 0 0 0 0 0 0 0 0 expression HLA-DQA1 −0.2 −0.4−0.1 0 0 −0.2 −0 −0.5 −0 expression TDP1 expression 0 0.5 0 −0.5 0.4 0 0−0 0.4 RC3H2 −0 0.4 0 −0.3 0 −0 0.2 −0 0.3 expression MTUS1 −0 −0 0 0.50 −0 0 −0 0 expression NR1H2 0.1 −0 −0.3 0 0 0.5 0 0 −0 expressionSUPT6H 0 −0.5 −0.4 0 −0.1 0.3 −0 −0 0.3 expression BCAT2 −0 −0 −0.5 −0 00.5 −0 −0 −0 expression LOC130576 0 0 0 0 0 0 0 0 0 expression PH-4expression 0.2 −0.4 0.3 −0 −0.5 0.1 0.3 −0 0.1 ARHGAP10 −0.5 0.1 0.4 0 0−0.1 −0 −0 0.3 expression TACC1 −0.5 0.5 −0 0 0.5 −0.2 0.4 0 0.3expression HLA-DQB1 −0.4 −0 −0.4 0 −0 −0 −0 −0.1 −0 expression ATP6V0A2−0.4 0.1 0.2 −0.4 0.5 −0 0.4 0.5 0.2 expression TFPI expression −0.3 0−0 −0 −0 −0 −0 −0 0 BDNFOS 0 0 0 0 0 0 0 0 0 expression HLA-Cw06 allele0 0 0 0 0 0 0 0 0 IL-1α expression −0 −0 −0 −0 −0 −0 −0 2 −0 level IL-1βexpression −0.1 −0.1 −2.1 −0.9 −1.5 −0 −0.1 −0 −0.1 level IL-6expression −1.2 0.4 −2.4 −0 −1.5 0.1 −0.7 −1.5 0.1 level TNF-αexpression −1.5 −1.6 −0.8 −1 −0.1 −0 −0 −0 −0.2 level MIP-1α expression−0 −0.2 −2.8 0 −2.1 −0.2 −0.1 −1.4 −0 level (CCL3) MIP-1β expression −2−0.2 −2.7 0.6 −0 0 −0 −0 0 level (CCL4) Performance 0 0 0 0 0 0 0 0 0status Interval from 0 0 0 0 0 0 0 0 0 initial diagnosis to treatmenSerum calcium 0 0 0 0 0 0 0 0 0 level Serum 0 0 0 0 0 0 0 0 0 hemoglobinlevel Osteopontin level −0 −0 2.9 −2.9 −0 −1.2 0 0 −0 (SPP1) TRAIL level−1.8 0 −0 0 −0 0 −0.1 0.3 0 (TNFSF10) VEGFR2 level 1.5 0 −0 0.4 0 0.2−0.1 0.4 −0 (KDR) VEGF level −1 0.1 −0 0.4 −0 0 0 0 −0 CAIX level −0.3−0 0.5 0 −0 −1.6 0.6 −0 0.6 (CA9) collagen IV level 0 0 −0 0.2 −0.2 1.70 0.2 0 (COL4A1; COL4A2; COL4A3; COL4A4; COL4A5) Ulceration of 0 0 0 0 00 0 0 0 primary Breslow thickness 0 0 0 0 0 0 0 0 0 STAT1 gene −0 0.12.4 −0 −0 0.4 −0.2 −0 0 expression MTAP gene −0 −2.9 1.4 −0 0.4 0 2.20.9 2.1 expression Ki-67 expression 0.3 −0 0 1.3 2.3 −0 0 −0 −2.9(MKI67) Neutrophil count 0 0 0 0 0 0 0 0 0 Leucocytes count −2.7 −0.8−0.8 −0 0 0.2 −0 −0.1 0 CD8+ CD57+ −2.4 −1.5 −2.2 0 0 0 −0 −1 −0 cellsnumber CD4+ cells −2.6 −1.9 −0 0 0.6 −0.8 0 −0 0.3 number CD83+ TIDCcells −1.9 −3 0.8 −2.9 0 0 0 2.9 2.9 number Hepatic RIG-1 0 0 0 0 0 0 00 0 expression (DDX58) Therapy Score −19 −10 −7.2 −3.6 −1 0.2 2.2 2 4.5EB- FS- W3- ER- GN- D3- FR- HR- TCGA A5SH A1ZT AA1Q A19M A4U5 A8GB A44AA2OH Response SD CPD CPD CR CR CR CR CR ID: 1643 1617 2101 1857 1156 9385299 2004 ID NO: 3 17 14 12 7 2 5 8 Delta32 CCR5 0 0 0 0 0 0 0 0Polymorphism CCR5 LOF 0 0 0 0 0 0 0 0 mutation IFN-g 0 0 0 0 0 0 0 0(+874A− > T) polymorphism IL-10 0 0 0 0 0 0 0 0 (−1082G− > A)polymorphism ERCC1 0 0 0 0 0 0 0 0 (codon 118) polymorphism VCAM1 −0 00.9 0.6 0 0 0.7 0.5 expression Platelets Number 0 0 0 0 0 0 0 0 Alkaline0 0 0 0 0 0 0 0 phosphatase level Sedimentation 0 0 0 0 0 0 0 0 rateWeight loss 0 0 0 0 0 0 0 0 Time from tumor 0 0 0 0 0 0 0 0 tooccurrence of metastases Number of 0 0 0 0 0 0 0 0 metastatic sites Bonemetastasis 0 0 0 0 0 0 0 0 Liver metastasis 0 0 0 0 0 0 0 0 Mediastinum0 0 0 0 0 0 0 0 metastasis GBF1 expression −0.4 −0 0.3 0 0 0.5 −0 −0LNPEP −0.5 −0 −0 0 0 −0.2 0 0 expression MAP3K5 −0.5 −0 0 0 0 −0 0.5 0expression cDNA FLJ37989 0 0 0 0 0 0 0 0 fis expression RABL2B −0 −0.30.2 −0.1 0 0 0.1 0 expression MEF2A −0.5 0 −0.2 0.1 −0 −0.2 0.3 0expression LOC399900 0 0 0 0 0 0 0 0 expression HLA-DQA1 −0 −0 0.1 −00.4 −0 0.5 0.3 expression TDP1 expression −0 0 −0.1 0.3 −0 −0.4 −0 0RC3H2 −0 0.3 −0 0 −0 −0.4 0.1 0.1 expression MTUS1 −0.4 −0 −0.5 0.1 0−0.2 0 0.4 expression NR1H2 −0.1 0.4 0 −0 −0 0 0 −0 expression SUPT6H 0−0 0.5 0 −0 0.4 0 −0 expression BCAT2 −0 0.1 0.5 0 0 −0 0.4 0 expressionLOC130576 0 0 0 0 0 0 0 0 expression PH-4 expression 0.5 −0 0.5 0.3 −0−0 −0 −0 ARHGAP10 −0.5 0.4 −0 −0.1 −0 0 −0 −0 expression TACC1 0 −0.1−0.1 −0 −0 −0.1 0 0 expression HLA-DQB1 −0 −0 0 0.4 0.4 0 0.5 0.5expression ATP6V0A2 −0.5 0 −0 0.2 −0 −0.3 0 −0 expression TFPIexpression −0.5 −0 −0.4 −0 −0 0 −0 −0 BDNFOS 0 0 0 0 0 0 0 0 expressionHLA-Cw06 allele 0 0 0 0 0 0 0 0 IL-1α expression −0 −0 −0 −0 0.3 −0 −0−0 level IL-1β expression −0.1 0 −1.4 −0 −0 −0 0 0 level IL-6 expression−1.4 0.9 −0 −0 −0 2.8 −0.1 0.2 level TNF-α expression −0 0 −0.2 −0.2 0−0 0 0.1 level MIP-1α expression −0 −0 −2.8 0 0 1.3 2.9 2.9 level (CCL3)MIP-1β expression −0.9 0 −0 2.5 2.3 1 3 3 level (CCL4) Performance 0 0 00 0 0 0 0 status Interval from 0 0 0 0 0 0 0 0 initial diagnosis totreatmen Serum calcium 0 0 0 0 0 0 0 0 level Serum 0 0 0 0 0 0 0 0hemoglobin level Osteopontin level 3 0.2 2.9 −1.6 −0 0 −0 0.3 (SPP1)TRAIL level −1.9 0 −0 1 1.2 0 1.8 2 (TNFSF10) VEGFR2 level 0 0 −0 0 1.20.3 0.1 0.1 (KDR) VEGF level 1.4 0 0.2 −0 1.4 0.3 0 0 CAIX level 0.6 −00.6 −0 −0 0 0.2 −0 (CA9) collagen IV level 1 0 1.9 0 0 −0 0 0.4 (COL4A1;COL4A2; COL4A3; COL4A4; COL4A5) Ulceration of 0 0 0 0 0 0 0 0 primaryBreslow thickness 0 0 0 0 0 0 0 0 STAT1 gene −3 0.5 −0 2.7 2.2 −0 2.92.9 expression MTAP gene 0 0.9 0.2 −2.8 −2.7 −0 −2.5 0 expression Ki-67expression 2.8 0 3 −0.9 0.6 2.9 0 −0.9 (MKI67) Neutrophil count 0 0 0 00 0 0 0 Leucocytes count 0 1.8 −0 −0 −0 2.7 1 2.7 CD8+ CD57+ −0 0.3 02.5 2.1 0.9 2.9 3 cells number CD4+ cells 0 1 2.6 2.7 2.7 0 0.3 3 numberCD83+ TIDC cells 1.5 −0.3 −1.7 0.4 0 0 0 −0 number Hepatic RIG-1 0 0 0 00 0 0 0 expression (DDX58) Therapy Score −0.4 6.4 7 8.1 12 11 16 21Abbreviations; PR—partial response, SD—stable disease, CR—completeresponse, and CPD—clinical progressive disease.

TABLE 9 Set of normalized biomarker values for each patient having anegative therapy score treated with anti-cancer vaccine therapy.Response NR NR NR NR NR NR NR NR R NR NR NR NR ID NO: 21 33 17 5 26 3425 22 65 27 3 14 19 TGFbeta level −0.05 2.13 −2.99 0.01 0 −0.17 0 0.361.42 −0.03 0.93 0.31 0 M1/M2 macrophage ratio −0 −0 −0 −0 −0 −0 −0 −0 1−0 1 −0 −0 T reg cell % 0 0 0 0 0 0 0 0 0 0 0 0 0 MDSC number 0 0 0 0 00 0 0 0 0 0 0 0 lymphocyte number −2.75 −2.76 −2.44 −1.24 −1.33 −0.07−2.64 −0.09 −2.34 −2.74 −1.06 −2.64 0.04 ECOG performance 0 0 0 0 0 0 00 0 0 0 0 0 score EGF level 0 0 0 0 0 0 0 0 0 0 0 0 0 Cancer-TestisAntigens' 0 0 0 0 0 0 0 0 0 0 0 0 0 Genes expression IFN-gamma-induced 00 0 0 0 0 0 0 0 0 0 0 0 tumor cell apoptosis IL-6 level 0 0 0 0 0 0 0 00 0 0 0 0 Mean Corpuscular 0 0 0 0 0 0 0 0 0 0 0 0 0 HemoglobinConcentration (MCHC) Patient's age 0 0 0 0 0 0 0 0 0 0 0 0 0 Predictivegene signature 0 0 0 0 0 0 0 0 0 0 0 0 0 in MAGE A3 antigen- specificcancer immunotherapy TGFbeta1 level 0 0 0 0 0 0 0 0 0 0 0 0 0CD16+CD56+CD69+ 0 0 0 0 0 0 0 0 0 0 0 0 0 lymphocytes number CD4+PD-1+ Tcell −1.37 0 −0 −0 −2.07 −0.07 0 −0.01 2.37 −1.19 −0.09 0.01 0.01number_1 C-reactive protein level 0 0 0 0 0 0 0 0 0 0 0 0 0 Intratumoralversus −2.82 −4.02 −4.13 −4.13 −4.55 −2.25 −4.02 −3.26 −3.45 −4.06 −3.12−2.97 −0.12 peritumoral T cell density Serum amyloid A level 0 0 0 0 0 00 0 0 0 0 0 0 Toll-like receptor 4 gene 0 0 0 0 0 0 0 0 0 0 0 0 0polymorphism Syndecan-4 mRNA 0 0 0 0 0 0 0 0 0 0 0 0 0 expression levelWT1 expression 0 0 0 0 0 0 0 0 0 0 0 0 0 Serum S100B 0 0 0 0 0 0 0 0 0 00 0 0 concentration LDH level 0 0 0 0 0 0 0 0 0 0 0 0 0 I/II high-gradeor III 0 0 0 0 0 0 0 0 0 0 0 0 0 T1/2/3a low-grade disease_intermediaterisk lymphocytes in PBMC % −0.92 −0.92 −0.81 −0.41 −0.44 −0.02 −0.88−0.03 −0.78 −0.91 −0.35 −0.88 0.01 PTEN loss −0.89 0.94 0 −0.96 1 0.980.01 −0 −0.13 1 0.13 0.15 −0.02 CD4+CD45RO+ cell 0 0 0 0 0 0 0 0 0 0 0 00 number Number of CD27− 0 0 0 0 0 0 0 0 0 0 0 0 0 CD45RA+ and CD27−CD45RA− and CD27+CD45RA− T-cells CD4+CTLA-4+ T cell 0 0 0 0 0 0 0 0 0 00 0 0 number CD4+PD-1+ T cell 0 0 0 0 0 0 0 0 0 0 0 0 0 number_1 IgM forBlood Group A 0 0 0 0 0 0 0 0 0 0 0 0 0 trisaccharide levelLin-CD14+HLA-DR−/ 0 0 0 0 0 0 0 0 0 0 0 0 0 lo MDSC level B2M −2.93−2.56 0.08 −0 −0.08 −2.54 0 −2.25 −0.22 0.79 −0.5 −0.04 −2.42 CD86 −2.93−2.53 −0.39 −2.78 −0.68 −2.91 −1.37 −0 −2.98 −0.43 −2.32 −1.09 −1.27CXCL10 −2.92 −2.91 −1.62 −2.27 −2.96 −2.92 −0.35 −2.72 −2.48 0.01 −1.97−0 −0.98 CXCL9 −2.76 −2.93 −2.93 −2.93 −2.91 −2.93 −1.94 −2.9 −1.66 0−0.03 −0 −0.02 Therapy Score −20.3 −15.6 −15.2 −14.7 −14 −12.9 −11.2−10.9 −9.25 −7.56 −7.39 −7.15 −4.75 Response NR NR NR NR NR R R NR R R RNR ID NO: 20 28 35 4 15 51 52 2 58 54 66 32 TGFbeta level 1.08 −2.91 0 01.57 −0 0.91 0.88 0.19 −0.83 −0.15 1.02 M1/M2 macrophage ratio −0 −0 −0−0 −0 −0 −0 −0 −0 −0 −0 −0 T reg cell % 0 0 0 0 0 0 0 0 0 0 0 0 MDSCnumber 0 0 0 0 0 0 0 0 0 0 0 0 lymphocyte number −0.87 −0 −0.01 −0.51−0.01 0.08 −0.09 −1.48 −0 0 −0.01 −0 ECOG performance 0 0 0 0 0 0 0 0 00 0 0 score EGF level 0 0 0 0 0 0 0 0 0 0 0 0 Cancer-Testis Antigens' 00 0 0 0 0 0 0 0 0 0 0 Genes expression IFN-gamma-induced 0 0 0 0 0 0 0 00 0 0 0 tumor cell apoptosis IL-6 level 0 0 0 0 0 0 0 0 0 0 0 0 MeanCorpuscular 0 0 0 0 0 0 0 0 0 0 0 0 Hemoglobin Concentration (MCHC)Patient's age 0 0 0 0 0 0 0 0 0 0 0 0 Predictive gene signature 0 0 0 00 0 0 0 0 0 0 0 in MAGE A3 antigen- specific cancer immunotherapyTGFbeta1 level 0 0 0 0 0 0 0 0 0 0 0 0 CD16+CD56+CD69+ 0 0 0 0 0 0 0 0 00 0 0 lymphocytes number CD4+PD-1+ T cell −1.08 −0 0.01 −0 −0.44 0.64−1.78 0 −0.92 0 −2.52 −1.73 number_1 C-reactive protein level 0 0 0 0 00 0 0 0 0 0 0 Intratumoral versus −0.69 −0.01 −0.45 −0.03 −2.17 0.38−1.34 −0.01 −0 −0.12 −0 0 peritumoral T cell density Serum amyloid Alevel 0 0 0 0 0 0 0 0 0 0 0 0 Toll-like receptor 4 gene 0 0 0 0 0 0 0 00 0 0 0 polymorphism Syndecan-4 mRNA 0 0 0 0 0 0 0 0 0 0 0 0 expressionlevel WT1 expression 0 0 0 0 0 0 0 0 0 0 0 0 Serum S100B 0 0 0 0 0 0 0 00 0 0 0 concentration LDH level 0 0 0 0 0 0 0 0 0 0 0 0 I/II high-gradeor III 0 0 0 0 0 0 0 0 0 0 0 0 T1/2/3a low-grade disease_intermediaterisk lymphocytes in PBMC % −0.29 −0 −0 −0.17 −0 0.03 −0.03 −0.49 −0 0 −0−0 PTEN loss −0.52 0 −0.49 0.69 0.01 −0 0.38 0.86 −0.89 −0.01 −0.95 −0CD4+CD45RO+ cell 0 0 0 0 0 0 0 0 0 0 0 0 number Number of CD27− 0 0 0 00 0 0 0 0 0 0 0 CD45RA+ and CD27− CD45RA− and CD27+CD45RA− T-cellsCD4+CTLA-4+ T cell 0 0 0 0 0 0 0 0 0 0 0 0 number CD4+PD-1+ T cell 0 0 00 0 0 0 0 0 0 0 0 number_1 IgM for Blood Group A 0 0 0 0 0 0 0 0 0 0 0 0trisaccharide level Lin-CD14+HLA-DR−/ 0 0 0 0 0 0 0 0 0 0 0 0 lo MDSClevel B2M 0 −0 0.04 0 0 −2.94 0.53 −0.93 0.15 0.01 1.33 0.21 CD86 −0.11−0.6 −2.18 −0.34 0 −0.25 −0 −0.04 0.03 −0 −0 0 CXCL10 −0.12 −0 −0.07−1.92 −0 0 0 −0.08 0.14 0 1.61 0.03 CXCL9 −1.05 0 −0.08 −0.35 −0.8 0.26−0.01 −0 0.01 −0 0.23 0.06 Therapy Score −3.65 −3.52 −3.23 −2.63 −1.84−1.81 −1.43 −1.31 −1.3 −0.95 −0.46 −0.42 Abbreviations; PR—partialresponse, SD—stable disease, CR—complete response, and CPD—clinicalprogressive disease.

TABLE 10 Set of normalized biomarker values for each patient having apositive therapy score treated with anti-cancer vaccine therapy.Response NR NR R R R NR NR R NR NR R NR NR R R NR ID NO: 29 8 55 63 5911 12 62 7 18 45 13 31 49 64 23 TGFbeta level 0.01 0 0.01 −1.7 0.04 1.42−1.57 1.25 0.53 −0.27 −0.45 −0.12 1.42 −0.04 0.38 −2.69 M1/M2 macrophageratio −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 T reg cell % 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 MDSC number 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0lymphocyte number 0.06 0.29 0.16 −0 0.31 −0 0.93 −0 0.06 0.21 0.12 0.110 0.41 0 0.22 ECOG performance 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 score EGFlevel 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cancer-Testis Antigens' 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 Genes expression IFN-gamma- 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 induced tumor cell apoptosis IL-6 level 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 Mean Corpuscular 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 HemoglobinConcentration (MCHC) Patient's age 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Predictive gene 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 signature in MAGE A3antigen-specific cancer immunotherapy TGFbeta1 level 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 CD16+CD56+CD69+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 lymphocytesnumber CD4+PD-1+ T cell 0 −0.06 −0.38 −0.3 0 −0.7 0.02 −0.04 −1.09 2.072.77 −0.19 1.64 −0 −0.66 −0.01 number_1 C-reactive protein level 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 Intratumoral versus −0 1.84 0 0 0 0.23 0 −0 0.040.01 0.15 0 −0 0.11 0.46 0.86 peritumoral T cell density Serum amyloid Alevel 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Toll-like receptor 4 gene 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 polymorphism Syndecan-4 mRNA 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 expression level WT1 expression 0 0 0 0 0 0 0 0 0 0 0 0 0 00 0 Serum S100B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 concentration LDH level0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I/II high-grade or III 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 T1/2/3a low-grade disease_intermediate risk lymphocytes inPBMC % 0.02 0.1 0.05 −0 0.1 −0 0.31 −0 0.02 0.07 0.04 0.04 0 0.14 0 0.07PTEN loss 0 0 0 −0.57 −0 0.45 −0 0 −0.29 −0.08 0.02 −0.01 −0.02 0 −00.42 CD4+CD45RO+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cell number Number ofCD27− 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CD45RA+ and CD27− CD45RA− andCD27+CD45RA− T-cells CD4+PD-1+ T cell 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0number_1 IgM for Blood 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Group Atrisaccharide level Lin-CD14+HLA-DR−/ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 loMDSC level B2M −0.01 −2.93 0.01 0.5 0.31 −1.13 0.02 0 0.69 0.19 0 0.050.02 0.66 1.74 0.83 CD86 −0.02 0.05 0.01 0.72 0 0.44 1.75 0.19 −0 0.040.25 0.05 0.76 0 1.15 1.53 CXCL10 −0 0 0.44 1.82 0.01 0.03 −0.25 0 1.130.28 −0.01 1.75 0.05 2.09 1.65 2.32 CXCL9 −0.01 1.22 0.53 0.48 0.22 0.41−0 −0 0.62 0.04 0 1.5 0 1.52 0.87 2.09 Therapy Score 0.05 0.5 0.83 0.951 1.17 1.21 1.39 1.7 2.55 2.87 3.16 3.87 4.88 5.59 5.64 Response NR R NRR R NR R R NR NR R R R R NR ID NO: 10 61 16 56 48 24 50 46 6 9 47 57 5360 30 TGFbeta level 0 0.22 2 −1.34 −0.23 1.42 0.49 −0 −2.9 −1.05 0.040.26 −0 0.08 −0 M1/M2 macrophage ratio −0 −0 −0 −0 −0 −0 −0 −0 −0 −0 −0−0 −0 −0 −0 T reg cell % 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 MDSC number 0 0 00 0 0 0 0 0 0 0 0 0 0 0 lymphocyte number 2.37 1.58 0 −0 2.99 2.87 1.640.01 0.12 2.74 0 2.96 2.92 2.63 2.68 ECOG performance 0 0 0 0 0 0 0 0 00 0 0 0 0 0 score EGF level 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cancer-TestisAntigens' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Genes expression IFN-gamma- 0 00 0 0 0 0 0 0 0 0 0 0 0 0 induced tumor cell apoptosis IL-6 level 0 0 00 0 0 0 0 0 0 0 0 0 0 0 Mean Corpuscular 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Hemoglobin Concentration (MCHC) Patient's age 0 0 0 0 0 0 0 0 0 0 0 0 00 0 Predictive gene 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 signature in MAGE A3antigen-specific cancer immunotherapy TGFbeta1 level 0 0 0 0 0 0 0 0 0 00 0 0 0 0 CD16+CD56+CD69+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 lymphocytesnumber CD4+PD-1+ T cell 2.7 −0 0 0 3 −0 −1.39 0 −0 2.99 2.62 2.8 −2.52 03 number_1 C-reactive protein level 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Intratumoral versus 4.69 2.59 0.75 0.08 4.99 2.83 0.42 3.69 4.17 4.65 04.93 4.89 4.69 4.43 peritumoral T cell density Serum amyloid A level 0 00 0 0 0 0 0 0 0 0 0 0 0 0 Toll-like receptor 4 gene 0 0 0 0 0 0 0 0 0 00 0 0 0 0 polymorphism Syndecan-4 mRNA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0expression level WT1 expression 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SerumS100B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 concentration LDH level 0 0 0 0 0 00 0 0 0 0 0 0 0 0 I/II high-grade or III 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0T1/2/3a low-grade disease_intermediate risk lymphocytes in PBMC % 0.790.53 0 −0 1 0.96 0.55 0 0.04 0.91 0 0.99 0.97 0.88 0.89 PTEN loss −00.66 −0.08 0.99 −0.83 −0 0 −0.41 −0.02 0 −0 −0.65 0 −0.02 −0.36CD4+CD45RO+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 cell number Number of CD27− 00 0 0 0 0 0 0 0 0 0 0 0 0 0 CD45RA+ and CD27− CD45RA− and CD27+CD45RA−T-cells CD4+PD-1+ T cell 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 number_1 IgM forBlood 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Group A trisaccharide levelLin-CD14+HLA- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 DR−/lo MDSC level B2M −30.15 1.36 2.61 −2.95 0.02 2.61 1.78 2 −2.38 2.12 0 1.29 2.27 0.02 CD86−0.03 0 0.41 1.28 0.55 1.36 2.04 2.27 2.53 2.7 2.35 1.79 2.84 2.2 2.89CXCL10 −0.81 0.13 1.63 2.78 −0 0.03 2.26 2.38 2.57 0.16 2.51 −0 2.082.24 2.76 CXCL9 0 0.98 1.85 1.66 0.19 0.04 2.02 1.04 2.26 1.25 2.37 0.022.27 2.02 2.18 Therapy Score 6.71 6.85 7.92 8.06 8.71 9.52 10.6 10.810.8 12 12 13.1 14.7 17 18.5 Abbreviations; PR—partial response,SD—stable disease, CR—complete response, and CPD—clinical progressivedisease.

TABLE 11 Set of normalized biomarker values for each patient treatedwith anti-angiogenic therapy. Patient ID GSM14718 39 86 53 58 75 42 8271 47 Response: NR NR NR NR R R NR R R Timepoint: PT PT PT PT PT PT PTPT PT Number of resting 0.000162 −1.97328 −0.00283 −1.3E−06 −0.67291−0.97508 −0.68776 −0.00018 0.533442 circulating endothelial cells Numberof total 0 0 0 0 0 0 0 0 0 circulating endothelial cells Expression of−2.97683 −0.00033 −0.00618 −0.69032 −0.001726 0.000607 0.004197 −1.2847−0.18186 PDGFR-beta Expression of 0 0.570456 0.000747 1.482181 02.460252 1.408502 0.000326 0 CD31 CDC16 level −9.4E−07 −0.11814 −1.604731.244301 −0.09602 3.4E−05 −1.0432 −0.02895 −1.6E−07 Lck and Fyn −0.968740.730858 −0.38819 −0.14019 1.06E−06 −0.18482 1.58E−05 0.00172 0.002242tyrosine kinases in initiation of TCR Activation pathway activation TCell Receptor 0 0 0 0 0 0 0 0 0 Signaling Pathway activation T HelperCell 0 0 0 0 0 0 0 0 0 Surface Molecules expression NO2-dependent 0 0 00 0 0 0 0 0 IL 12 Pathway activation in NK cells Bioactive Peptide 0 0 00 0 0 0 0 0 Induced Signaling Pathway sVEGFR1 −2.99459 −0.2038 1.9328550.000431 −0.01157 −0.33766 −2.89576 1.5E−08 0.310332 CD133 0 0 0 0 0 0 00 0 expression rs2286455 0 0 0 0 0 0 0 0 0 rs3130 0 0 0 0 0 0 0 0 0 IL-6plasma level 0 0 0 0 0 0 0 0 0 IL-8 plasma level 0 0 0 0 0 0 0 0 0Child-Pugh class 0 0 0 0 0 0 0 0 0 HBV status 0 0 0 0 0 0 0 0 0 portalvein 0 0 0 0 0 0 0 0 0 thrombosis Sex 0 0 0 0 0 0 0 0 0 History ofalcohol 0 0 0 0 0 0 0 0 0 intake Acneiform rash 0 0 0 0 0 0 0 0 0angiopoietin-2 −8.4E−11 −0.81987 −0.2002 −2.53227 0.001005 −2.10171−2.94843 −0.12168 3.27E−06 expression levels EGFR expression −0.98928−0.34509 1.07E−06 −0.00033 0.045048 0.098944 0.934068 0.009977 0.068077levels Endothelin-1 0.754617 −4.3E−08 −0.02217 −0.73196 0.198087−0.00242 −0.03232 0.002418 0.170778 expression levels angiopoietin-2 0 00 0 0 0 0 0 0 expression levels IL-12 plasma 0 0 0 0 0 0 0 0 0 level HGFplasma level 0 0 0 0 0 0 0 0 0 IL-16 plasma 0 0 0 0 0 0 0 0 0 levelCXCL10 plasma 0 0 0 0 0 0 0 0 0 level SDF-1α plasma 0 0 0 0 0 0 0 0 0level IL-2Rα plasma 0 0 0 0 0 0 0 0 0 level IL-3 plasma level 0 0 0 0 00 0 0 0 IFN-α2 plasma 0 0 0 0 0 0 0 0 0 level TRAIL plasma 0 0 0 0 0 0 00 0 level M-CSF plasma 0 0 0 0 0 0 0 0 0 level PIGF plasma 0 0 0 0 0 0 00 0 level mucinous 0 0 0 0 0 0 0 0 0 histology VEGF -1498 C > T 0 0 0 00 0 0 0 0 Liver metastasis 0 0 0 0 0 0 0 0 0 ECOG 0 0 0 0 0 0 0 0 0Performance Status VEGF -1154 A > G 0 0 0 0 0 0 0 0 0 VEGF G-634C 0 0 00 0 0 0 0 0 ICAM1 T469C 0 0 0 0 0 0 0 0 0 WNK1- 0 0 0 0 0 0 0 0 0rs11064560 EGF A-61G 0 0 0 0 0 0 0 0 0 CXCR2 C785T 0 0 0 0 0 0 0 0 0VEGF-1154 G/A 0 0 0 0 0 0 0 0 0 VEGF-2578 C/A 0 0 0 0 0 0 0 0 0 rs699946in 0 0 0 0 0 0 0 0 0 VEGFA rs12505758 in 0 0 0 0 0 0 0 0 0 VEGFR2 VEGFR10 0 0 0 0 0 0 0 0 rs9582036 EGF rs444903 0 0 0 0 0 0 0 0 0 A > G IGF-1rs6220 0 0 0 0 0 0 0 0 0 A > G CXCR1 0 0 0 0 0 0 0 0 0 rs2234671 G > CCXCR2 0 0 0 0 0 0 0 0 0 rs2230054 T > C EGFR rs2227983 0 0 0 0 0 0 0 0 0G > A VEGFR-2 0 0 0 0 0 0 0 0 0 rs2305948 C > T IL-8 251 T > A 0 0 0 0 00 0 0 0 CXCR2 C785T 0 0 0 0 6 0 0 0 0 VEGF C936T 0 0 0 0 0 0 0 0 0Adrenomedullin 0 0 0 0 0 0 0 0 0 Repeat Polymorphism VEGFA 0 0 0 0 0 02.259422 1.028465 1.81E−10 ICAM1 −0.27451 0.000147 −1.3524 −1.2E−09−0.35542 −0.304107 2.516688 −3.6E−12 −0.9676 Therapy Score −7.44919−2.15904 −1.64309 −1.36816 −0.89005 −0.73775 −0.48458 −0.40637 −0.06459Patient ID GSM14718 50 64 67 60 80 74 77 55 Response: NR NR NR R R NR RR Timepoint: PT PT PT PT PT PT PT PT Number of resting −0.00749 0.873339−1.67907 0.143095 −1.93425 1.318883 −1.7134 1.698203 circulatingendothelial cells Number of total 0 0 0 0 0 0 0 0 circulatingendothelial cells Expression of −0.38181 0.00909 −0.26866 −0.0735−4.8E−09 0.14841 1.195321 1.614482 PDGFR-beta Expression of 3.95E−05 03.95E−05 0 2.986855 0 0 2.741311 CD31 CDC16 level −0.04842 −0.06151.26E−06  3.4E−05 0.850735 1.787078 0.080641 1.974031 Lck and Fyn1.33E−09 −3.5E−06 0.097905 2.49E−08 0.949367 −0.4284 0.082804 −0.98781tyrosine kinases in initiation of TCR Activation pathway activation TCell Receptor 0 0 0 0 0 0 0 0 Signaling Pathway activation T Helper Cell0 0 0 0 0 0 0 0 Surface Molecules expression NO2-dependent 0 0 0 0 0 0 00 IL 12 Pathway activation in NK cells Bioactive Peptide 0 0 0 0 0 0 0 0Induced Signaling Pathway sVEGFR1 0.974816 0.592048 0.241733 1.68231−2.93689 −2.7E−10 −6.2E−08 −0.0023 CD133 0 0 0 0 0 0 0 0 expressionrs2286455 0 0 0 0 0 0 0 0 rs3130 0 0 0 0 0 0 0 0 IL-6 plasma level 0 0 00 0 0 0 0 IL-8 plasma level 0 0 0 0 0 0 0 0 Child-Pugh class 0 0 0 0 0 00 0 HBV status 0 0 0 0 0 0 0 0 portal vein 0 0 0 0 0 0 0 0 thrombosisSex 0 0 0 0 0 0 0 0 History of alcohol 0 0 0 0 0 0 0 0 intake Acneiformrash 0 0 0 0 0 0 0 0 angiopoietin-2 −0.24052 0.360074 1.61E−14 0.000457−0.42763 0.000343 −0.00322 −2.97733 expression levels EGFR expression0.001253 0.515136 −0.98413 0.017333 0.376318 −0.27847 −0.92899 −0.08396levels Endothelin-1 −2.7E−06 0.890757 0.707517 0.085635 −0.398970.077677 −0.99431 −0.94965 expression levels angiopoietin-2 0 0 0 0 0 00 0 expression levels IL-12 plasma 0 0 0 0 0 0 0 0 level HGF plasmalevel 0 0 0 0 0 0 0 0 IL-16 plasma 0 0 0 0 0 0 0 0 level CXCL10 plasma 00 0 0 0 0 0 0 level SDF-1α plasma 0 0 0 0 0 0 0 0 level IL-2Rα plasma 00 0 0 0 0 0 0 level IL-3 plasma level 0 0 0 0 0 0 0 0 IFN-α2 plasma 0 00 0 0 0 0 0 level TRAIL plasma 0 0 0 0 0 0 0 0 level M-CSF plasma 0 0 00 0 0 0 0 level PIGF plasma 0 0 0 0 0 0 0 0 level mucinous 0 0 0 0 0 0 00 histology VEGF -1498 C > T 0 0 0 0 0 0 0 0 Liver metastasis 0 0 0 0 00 0 0 ECOG 0 0 0 0 0 0 0 0 Performance Status VEGF -1154 A > G 0 0 0 0 00 0 0 VEGF G-634C 0 0 0 0 0 0 0 0 ICAM1 T469C 0 0 0 0 0 0 0 0 WNK1- 0 00 0 0 0 0 0 rs11064560 EGF A-61G 0 0 0 0 0 0 0 0 CXCR2 C785T 0 0 0 0 0 00 0 VEGF-1154 G/A 0 0 0 0 0 0 0 0 VEGF-2578 C/A 0 0 0 0 0 0 0 0 rs699946in 0 0 0 0 0 0 0 0 VEGFA rs12505758 in 0 0 0 0 0 0 0 0 VEGFR2 VEGFR1 0 00 0 0 0 0 0 rs9582036 EGF rs444903 0 0 0 0 0 0 0 0 A > G IGF-1 rs6220 00 0 0 0 0 0 0 A > G CXCR1 0 0 0 0 0 0 0 0 rs2234671 G > C CXCR2 0 0 0 00 0 0 0 rs2230054 T > C EGFR rs2227983 0 0 0 0 0 0 0 0 G > A VEGFR-2 0 00 0 0 0 0 0 rs2305948 C > T IL-8 251 T > A 0 0 0 0 0 0 0 0 CXCR2 C785T 00 0 0 0 0 0 0 VEGF C936T 0 0 0 0 0 0 0 0 Adrenomedullin 0 0 0 0 0 0 0 0Repeat Polymorphism VEGFA 0 0.007774 0 0 4.64E−06 0.182942 2.982064 0ICAM1 −0.00017 −2.45837 2.727169 −0.06574 −2.997447 0.000147 2.144079−0.00516 Therapy Score −0.297699 0.728342 0.84251 1.789627 2.4629872.808607 2.844987 3.021824 Abbreviations; NR—no response, PR—partialresponse, SD—stable disease, CR—complete response, and CPD—clinicalprogressive disease.

REFERENCES

-   Hugo et al., Genomic and Transcriptomic Features of Response to    Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 165, 35-44 (2016).-   Van Allen et al., Genomic Correlates of Response to CTLA-4 Blockade    in Metastatic Melanoma. Science. 350(6257):302-22 (2015).

ILLUSTRATIVE EMBODIMENTS

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker in at least a reference subset of a plurality ofbiomarkers across a respective group of people, each of the plurality ofbiomarkers being associated with at least one therapy in a plurality oftherapies; determining, using the sequencing data and the biomarkerinformation, a normalized score for each biomarker in at least a subjectsubset of the plurality of biomarkers to obtain a set of normalizedbiomarker scores for the subject, wherein the subject subset of theplurality of biomarkers is a subset of the reference subset of theplurality of biomarkers; and determining, using the set of normalizedbiomarker scores for the subject, therapy scores for the plurality oftherapies, each of the therapy scores indicative of predicted responseof the subject to administration of a respective therapy in theplurality of therapies.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker in at least areference subset of a plurality of biomarkers across a respective groupof people, each of the plurality of biomarkers being associated with atleast one therapy in a plurality of therapies; determining, using thesequencing data and the biomarker information, a normalized score foreach biomarker in at least a subject subset of the plurality ofbiomarkers to obtain a set of normalized biomarker scores for thesubject, wherein the subject subset of the plurality of biomarkers is asubset of the reference subset of the plurality of biomarkers; anddetermining, using the set of normalized biomarker scores for thesubject, therapy scores for the plurality of therapies, each of thetherapy scores indicative of predicted response of the subject toadministration of a respective therapy in the plurality of therapies.

In one aspect provided herein is a method, comprising using at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker in at least a reference subset of a plurality ofbiomarkers across a respective group of people, each of the plurality ofbiomarkers being associated with at least one therapy in a plurality oftherapies; determining, using the sequencing data and the biomarkerinformation, a normalized score for each biomarker in at least a subjectsubset of the plurality of biomarkers to obtain a set of normalizedbiomarker scores for the subject, wherein the subject subset of theplurality of biomarkers is a subset of the reference subset of theplurality of biomarkers; and determining, using the set of normalizedbiomarker scores for the subject, therapy scores for the plurality oftherapies, each of the therapy scores indicative of predicted responseof the subject to administration of a respective therapy in theplurality of therapies.

In some embodiments, the plurality of biomarkers includes a firstbiomarker, and determining a normalized score for each biomarker in atleast the subject subset of the plurality of biomarkers comprises:determining a first normalized score for the first biomarker using thedistribution of values for the first biomarker. In some embodiments,determining the first normalized score comprises: determining a firstun-normalized score for the first biomarker using the sequencing data;determining a first Z-score based on the first distribution of valuesfor the first biomarker; and determining the first normalized score forthe first biomarker based on the first un-normalized score and the firstZ-score.

In some embodiments, determining therapy scores for the plurality oftherapies comprises determining a first therapy score for a firsttherapy in the plurality of therapies as a sum of two or more scores inthe set of normalized biomarker scores for the subject.

In some embodiments, determining therapy scores for the plurality oftherapies comprises determining a first therapy score for a firsttherapy in the plurality of therapies at least in part by: determiningweights for two or more scores in the set of normalized biomarker scoresfor the subject; and determining the first therapy score as a weightedsum of the two or more scores, summands of the sum being weighted by thedetermined weights.

In some embodiments, determining the weights comprises determining theweights using a statistical model. In some embodiments, determining theweights comprises determining the weights using a generalized linearmodel. In some embodiments, determining the weights comprisesdetermining the weights using a logistic regression model.

In some embodiments, the plurality of therapies comprises a firsttherapy and a second therapy different from the first therapy, andwherein determining therapy scores for the plurality of therapiescomprises: determining a first therapy score for the first therapy usinga first subset of the set of normalized biomarker scores for thesubject; and determining a second therapy score for the second therapyusing a second subset of the set of normalized biomarker scores for thesubject, wherein the second subset is different from the first subset.

Some embodiments include providing the determined therapy scores to auser. Some embodiments include ranking the plurality of therapies basedon the determined therapy scores. Some embodiments include recommendingat least one of the plurality of therapies for the subject based on thedetermined therapy scores.

In some embodiments, recommending the at least one of the plurality oftherapies comprises: ranking the plurality of therapies based on thedetermined therapy scores; and recommending at least a threshold numberof top-ranked therapies for the subject.

In some embodiments, the plurality of therapies comprises at least twotherapies selected from the group consisting of: an anti-PD1 therapy, ananti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy, ananti-cancer vaccine therapy, an anti-angiogenic therapy, and ananti-CD20 therapy.

In some embodiments, the plurality of biomarkers associated with theanti-PD1 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-PD1 therapy in Table 2. In someembodiments, determining the normalized biomarker scores for the subjectcomprises determining a normalized score for each of at least threebiomarkers selected from the group of biomarkers associated withanti-PD1 therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-CTLA4 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-CTLA4 therapy in Table 2. Insome embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CTLA4 therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theIL-2 therapy comprises at least three biomarkers selected from the groupof biomarkers associated with IL-2 therapy in Table 2. In someembodiments, determining the normalized biomarker scores for the subjectcomprises determining a normalized score for each of at least threebiomarkers selected from the group of biomarkers associated with IL-2therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with the IFNalpha therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with IFN alpha therapy in Table 2. Insome embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withIFN alpha therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-cancer vaccine therapy comprises at least three biomarkers selectedfrom the group of biomarkers associated with anti-cancer vaccine therapyin Table 2. In some embodiments, determining the normalized biomarkerscores for the subject comprises determining a normalized score for eachof at least three biomarkers selected from the group of biomarkersassociated with anti-cancer vaccine therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-angiogenic therapy comprises at least three biomarkers selectedfrom the group of biomarkers associated with anti-angiogenic therapy inTable 2. In some embodiments, determining the normalized biomarkerscores for the subject comprises determining a normalized score for eachof at least three biomarkers selected from the group of biomarkersassociated with anti-angiogenic therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-CD20 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-CD20 therapy in Table 2. Insome embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CD20 therapy in Table 2. In some embodiments, the anti-CD20 therapyis rituximab.

Some embodiments further include generating a graphical user interface(GUI) comprising: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In some embodiments, the at least one visual characteristic comprisescolor of a GUI element and/or size of the GUI element.

In some embodiments, in response to receiving, via the GUI, a userselection of the first therapy, presenting, via the GUI, informationabout at least one biomarker with which at least one of the firstplurality of GUI elements is associated.

In some embodiments, the first therapy is associated with a firsttherapy score and the second therapy is associated with a second therapyscore, and wherein the first portion and the second portion arepositioned, relative to one another in the GUI, based on relativemagnitude of the first therapy score and the second therapy score.

In some embodiments, each of the plurality of biomarkers is selectedfrom the group consisting of: a genetic biomarker, a cellular biomarker,a saccharide biomarker, a lipid biomarker, a heterocyclic biomarker, anelementary compound biomarker, an imaging biomarker, an anthropologicalbiomarker, a personal habit biomarker, a disease-state biomarker, and anexpression biomarker. In some embodiments, the one or more geneticbiomarkers includes a gene or marker described in the description and/orthe figures.

In some embodiments, one or more genetic biomarkers are selected fromthe group consisting of: interferons, cytotoxic proteins, enzymes, celladhesion proteins, extracellular matrix proteins and polysaccharides,cell growth factors, cell differentiation factors, transcriptionfactors, and intracellular signaling proteins. In some embodiments, theone or more genetic biomarkers is selected from the group consisting of:a cytokine, a chemokine, a chemokine receptor, and an interleukin. Insome embodiments, the value of one or more cellular biomarkers isdetermined through analysis of the number of one or more types of cellsor the percentage of one or more types of cells within the biologicalsample. In some embodiments, the one or more types of cells are selectedfrom the group consisting of malignant cancerous cells, leukocytes,lymphocytes, stromal cells, vascular endothelial cells, vascularpericytes, and myeloid-derived suppressor cells (MDSCs). In someembodiments, the value of one or more expression biomarkers isdetermined through analysis of the expression level or enzymaticactivity of the nucleic acid or protein of the expression biomarker.

In some embodiments, the sequencing data is one or more of: DNAsequencing data, RNA sequencing data, or proteome sequencing data. Insome embodiments, the sequencing data is obtained using one or more ofthe following techniques: whole genome sequencing (WGS), whole exomesequencing (WES), whole transcriptome sequencing, mRNA sequencing,DNA/RNA-hybridization, microarray, DNA/RNA chip, PCR, and singlenucleotide polymorphism (SNP) genotyping.

In some embodiments each of the at least one biological samples is abodily fluid, a cell sample, a liquid biopsy, or a tissue biopsy. Insome embodiments, the tissue biopsy comprises one or more samples fromone or more tumors or tissues known or suspected of having cancerouscells.

In some embodiments, the biomarker information also comprises resultsfrom one or more of the following types of analyses: blood analysis,cytometry analysis, histological analysis, immunohistological analysis,and patient history analysis.

In some embodiments, each of the therapies are selected from the groupconsisting of: surgery, radiation therapy, chemotherapy, immunotherapy,viral therapy, targeted therapy, hormone therapy, transplants,phototherapy, cryotherapy, and hyperthermia. In some embodiments, eachof the therapies are selected from immunotherapy and targeted therapy.

In some embodiments, the therapy scores are indicative of response ofthe subject to administration of one therapy in the plurality oftherapies. In some embodiments, the therapy scores are indicative ofpredicted response of the subject to administration of multipletherapies in the plurality of therapies.

In one aspect provided herein is a system comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining first sequencing dataabout at least one biological sample of a subject prior toadministration of a candidate therapy; obtaining second sequencing dataabout at least one other biological sample of the subject subsequent toadministration of the candidate therapy; accessing, in the at least onedatabase, biomarker information indicating a distribution of values foreach biomarker, across a respective group of people, in at least areference subset of a plurality of biomarkers; determining, using thefirst and second sequencing data and the biomarker information, a firstset of normalized biomarker scores for the subject and a second set ofnormalized biomarker scores for the subject; and determining, using thefirst and second sets of normalized biomarker scores for the subject, animpact score for the candidate therapy, wherein the impact score isindicative of response of the subject to administration of the candidatetherapy.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining first sequencing data about at least one biologicalsample of a subject prior to administration of a candidate therapy;obtaining second sequencing data about at least one other biologicalsample of the subject subsequent to administration of the candidatetherapy; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker, across arespective group of people, in at least a reference subset of aplurality of biomarkers; determining, using the first and secondsequencing data and the biomarker information, a first set of normalizedbiomarker scores for the subject and a second set of normalizedbiomarker scores for the subject; and determining, using the first andsecond sets of normalized biomarker scores for the subject, an impactscore for the candidate therapy, wherein the impact score is indicativeof response of the subject to administration of the candidate therapy.

In one aspect provided herein is a method, comprising: using at leastone computer hardware processor to perform: obtaining first sequencingdata about at least one biological sample of a subject prior toadministration of a candidate therapy; obtaining second sequencing dataabout at least one other biological sample of the subject subsequent toadministration of the candidate therapy; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker, across a respective group of people, in at least areference subset of a plurality of biomarkers; determining, using thefirst and second sequencing data and the biomarker information, a firstset of normalized biomarker scores for the subject and a second set ofnormalized biomarker scores for the subject; and determining, using thefirst and second sets of normalized biomarker scores for the subject, animpact score for the candidate therapy, wherein the impact score isindicative of response of the subject to administration of the candidatetherapy.

In some embodiments, determining the impact score for the candidatetherapy further comprises: determining, using the first and second setsof normalized biomarker scores, a difference score for each biomarker inat least a subject subset of the plurality of biomarkers to obtain a setof biomarker difference scores for the subject; and determining, usingthe set of biomarker difference scores, the impact score for thecandidate therapy.

In some embodiments, determining the impact score for the candidatetherapy further comprises: determining, using the first and second setsof normalized biomarker scores, a first and second subject subset scorefor the subject subset of the plurality of biomarkers determining asubject subset difference score, wherein the subject subset differencescore is determined using the first and second subject subset score; anddetermining, using the subject subset difference score, the impact scorefor the candidate therapy.

In some embodiments, the candidate therapy is selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, and an anti-CD20 therapy.

In some embodiments, the plurality of biomarkers associated with theanti-PD1 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-PD1 therapy in Table 2.

In some embodiments, determining the biomarker difference scores for thesubject comprises determining a difference score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-PD1 therapy in Table 2.

In some embodiments, determining the subject subset difference score forthe subject comprises determining a first and second subject subsetscore for at least three biomarkers selected from the group ofbiomarkers associated with anti-PD1 therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-CTLA4 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-CTLA4 therapy in Table 2. Insome embodiments, determining the biomarker difference scores for thesubject comprises determining a difference score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CTLA4 therapy in Table 2. In some embodiments, determining thesubject subset difference score for the subject comprises determining afirst and second subject subset score for at least three biomarkersselected from the group of biomarkers associated with anti-CTLA4 therapyin Table 2.

In some embodiments, the plurality of biomarkers associated with theIL-2 therapy comprises at least three biomarkers selected from the groupof biomarkers associated with IL-2 therapy in Table 2. In someembodiments, determining the biomarker difference scores for the subjectcomprises determining a difference score for each of at least threebiomarkers selected from the group of biomarkers associated with IL-2therapy in Table 2. In some embodiments, determining the subject subsetdifference score for the subject comprises determining a first andsecond subject subset score for at least three biomarkers selected fromthe group of biomarkers associated with IL-2 therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with the IFNalpha therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with IFN alpha therapy in Table 2. Insome embodiments, determining the biomarker difference scores for thesubject comprises determining a difference score for each of at leastthree biomarkers selected from the group of biomarkers associated withIFN alpha therapy in Table 2. In some embodiments, determining thesubject subset difference score for the subject comprises determining afirst and second subject subset score for at least three biomarkersselected from the group of biomarkers associated with IFN alpha therapyin Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-cancer vaccine therapy comprises at least three biomarkers selectedfrom the group of biomarkers associated with anti-cancer vaccine therapyin Table 2. In some embodiments, determining the biomarker differencescores for the subject comprises determining a difference score for eachof at least three biomarkers selected from the group of biomarkersassociated with anti-cancer vaccine therapy in Table 2. In someembodiments, determining the subject subset difference score for thesubject comprises determining a first and second subject subset scorefor at least three biomarkers selected from the group of biomarkersassociated with anti-cancer vaccine therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-angiogenic therapy comprises at least three biomarkers selectedfrom the group of biomarkers associated with anti-angiogenic therapy inTable 2. In some embodiments, determining the biomarker differencescores for the subject comprises determining a difference score for eachof at least three biomarkers selected from the group of biomarkersassociated with anti-angiogenic therapy in Table 2. In some embodiments,determining the subject subset difference score for the subjectcomprises determining a first and second subject subset score for atleast three biomarkers selected from the group of biomarkers associatedwith anti-angiogenic therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-CD20 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-CD20 therapy in Table 2. Insome embodiments, wherein determining the biomarker difference scoresfor the subject comprises determining a difference score for at leastthree biomarkers selected from the group of biomarkers associated withanti-CD20 therapy in Table 2. In some embodiments, wherein determiningthe subject subset difference score for the subject comprisesdetermining a first and second subject subset score for at least threebiomarkers selected from the group of biomarkers associated withanti-CD20 therapy in Table 2. In some embodiments, the anti-CD20 therapyis rituximab.

Some embodiments include generating a graphical user interface (GUI)comprising a first portion associated with the candidate therapy, thefirst portion including a first plurality of GUI elements, each of thefirst plurality of GUI elements being associated with a respectivebiomarker in the plurality of biomarkers and having at least one visualcharacteristic determined based on a difference score of the respectivebiomarker; and displaying the generated GUI.

Some embodiments include generating a graphical user interface (GUI)comprising: a first portion associated with the candidate therapy, thefirst portion including a first plurality of GUI elements, each of thefirst plurality of GUI elements being associated with a respectivebiomarker in the plurality of biomarkers and having at least one visualcharacteristic determined based on a subject subset difference score;and displaying the generated GUI. In some embodiments, the at least onevisual characteristic comprises color of a GUI element and/or size ofthe GUI element. Some embodiments include, in response to receiving, viathe GUI, a user selection of the candidate therapy, presenting, via theGUI, information about at least one biomarker with which at least one ofthe first plurality of GUI elements is associated.

In some embodiments, determining the difference score for each biomarkerin at least the subject subset comprises: determining a first normalizedscore for a first biomarker using the first sequencing data; determininga second normalized score for the first biomarker using the secondsequencing data; and determining a first difference score based on adifference between the first and second normalized scores.

In some embodiments, determining the difference score for each biomarkerin at least the subject subset comprises: determining a first subjectsubset score for at least three biomarkers using the first sequencingdata; determining a second subject subset score for at least threebiomarkers using the second sequencing data; and determining a firstsubject subset difference score based on a difference between the firstand second subject subset scores.

In some embodiments, the biomarker information includes a firstdistribution of values for the first biomarker across a first group ofpeople, and wherein determining the first normalized score comprises:determining a first un-normalized score for the first biomarker usingthe first sequencing data; determining a first Z-score based on thefirst distribution of values for the first biomarker; and determiningthe first normalized score for the first biomarker based on the firstun-normalized score and the first Z-score.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker in at least a reference subset of a plurality ofbiomarkers across a respective group of people, each of the plurality ofbiomarkers being associated with at least one therapy in a plurality oftherapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized scores for a first set ofbiomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized scores for a second set ofbiomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalized scoresas input to a statistical model to obtain a first therapy score for thefirst therapy; providing the second set of normalized scores as input tothe statistical model to obtain a second therapy score for the secondtherapy; generating a graphical user interface (GUI), wherein the GUIcomprises: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker in at least areference subset of a plurality of biomarkers across a respective groupof people, each of the plurality of biomarkers being associated with atleast one therapy in a plurality of therapies; determining, using thesequencing data and the biomarker information: a first set of normalizedscores for a first set of biomarkers associated with a first therapy inthe plurality of therapies; and a second set of normalized scores for asecond set of biomarkers associated with a second therapy in theplurality of therapies, wherein the first set of biomarkers is differentfrom the second set of biomarkers; providing the first set of normalizedscores as input to a statistical model to obtain a first therapy scorefor the first therapy; providing the second set of normalized scores asinput to the statistical model to obtain a second therapy score for thesecond therapy; generating a graphical user interface (GUI), wherein theGUI comprises: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In one aspect provided herein is a method, comprising using the at leastone computer hardware processor to perform: obtaining sequencing dataabout at least one biological sample of a subject; accessing, in atleast one database, biomarker information indicating a distribution ofvalues for each biomarker in at least a reference subset of a pluralityof biomarkers across a respective group of people, each of the pluralityof biomarkers being associated with at least one therapy in a pluralityof therapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized scores for a first set ofbiomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized scores for a second set ofbiomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalized scoresas input to a statistical model to obtain a first therapy score for thefirst therapy; providing the second set of normalized scores as input tothe statistical model to obtain a second therapy score for the secondtherapy; generating a graphical user interface (GUI), wherein the GUIcomprises: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In some embodiments, the plurality of biomarkers includes a firstbiomarker, and wherein determining a normalized score for each biomarkerin at least the subject subset of the plurality of biomarkers comprises:determining a first normalized score for the first biomarker using thedistribution of values for the first biomarker. In some embodiments,determining the first normalized score comprises: determining anun-normalized score for the first biomarker using the sequencing data;determining a Z-score based on the first distribution of values for thefirst biomarker; and determining a normalized score for the firstbiomarker based on the un-normalized score and the Z-score.

In some embodiments, determining therapy scores for the plurality oftherapies comprises determining a first therapy score for a firsttherapy in the plurality of therapies as a sum of two or more scores inthe set of normalized biomarker scores for the subject.

In some embodiments, determining therapy scores for the plurality oftherapies comprises determining a first therapy score for a firsttherapy in the plurality of therapies at least in part by: determiningweights for two or more scores in the set of normalized biomarker scoresfor the subject; and determining the first therapy score as a sum of thetwo or more scores, summands of the sum being weighted by the determinedweights. In some embodiments, determining the weights comprisesdetermining the weights using a machine learning technique. In someembodiments, determining the weights comprises determining the weightsusing a generalized linear model. In some embodiments, determining theweights comprises determining the weights using a logistic regressionmodel.

In some embodiments, the plurality of therapies comprises a firsttherapy and a second therapy different from the first therapy, andwherein determining therapy scores for the plurality of therapiescomprises: determining a first therapy score for the first therapy usinga first subset of the set of normalized biomarker scores for thesubject; and determining a second therapy score for the second therapyusing a second subset of the set of normalized biomarker scores for thesubject, wherein the second subset is different from the first subset.

Some embodiments include recommending at least one of the plurality oftherapies for the subject based on the determined therapy scores. Someembodiments include ranking the plurality of therapies based on thedetermined therapy scores. In some embodiments, recommending the atleast one of the plurality of therapies comprises: ranking the pluralityof therapies based on the determined therapy scores; and recommending atleast a threshold number of top-ranked therapies for the subject.

In some embodiments, the plurality of therapies comprise at least twotherapies selected from the group consisting of: an anti-PD1 therapy, ananti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy, ananti-cancer vaccine therapy, an anti-angiogenic therapy, and ananti-CD20 therapy.

In some embodiments, the plurality of biomarkers associated with theanti-PD1 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-PD1 therapy in Table 2. In someembodiments, determining the normalized biomarker scores for the subjectcomprises determining a normalized score for each of at least threebiomarkers selected from the group of biomarkers associated withanti-PD1 therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-CTLA4 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-CTLA4 therapy in Table 2. Insome embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CTLA4 therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theIL-2 therapy comprises at least three biomarkers selected from the groupof biomarkers associated with IL-2 therapy in Table 2. In someembodiments, determining the normalized biomarker scores for the subjectcomprises determining a normalized score for each of at least threebiomarkers selected from the group of biomarkers associated with IL-2therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with the IFNalpha therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with IFN alpha therapy in Table 2. Insome embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withIFN alpha therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-cancer vaccine therapy comprises at least three biomarkers selectedfrom the group of biomarkers associated with anti-cancer vaccine therapyin Table 2. In some embodiments, determining the normalized biomarkerscores for the subject comprises determining a normalized score for eachof at least three biomarkers selected from the group of biomarkersassociated with anti-cancer vaccine therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-angiogenic therapy comprises at least three biomarkers selectedfrom the group of biomarkers associated with anti-angiogenic therapy inTable 2. In some embodiments, determining the normalized biomarkerscores for the subject comprises determining a normalized score for eachof at least three biomarkers selected from the group of biomarkersassociated with anti-angiogenic therapy in Table 2.

In some embodiments, the plurality of biomarkers associated with theanti-CD20 therapy comprises at least three biomarkers selected from thegroup of biomarkers associated with anti-CD20 therapy in Table 2. Insome embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CD20 therapy in Table 2. In some embodiments, the anti-CD20 therapyis rituximab.

In some embodiments, the at least one visual characteristic comprisescolor of a GUI element and/or size of the GUI element.

In some embodiments, in response to receiving, via the GUI, a userselection of the first therapy, presenting, via the GUI, informationabout at least one biomarker with which at least one of the firstplurality of GUI elements is associated.

In some embodiments, the first therapy is associated with a firsttherapy score and the second therapy is associated with a second therapyscore, and wherein the first portion and the second portion arepositioned, relative to one another in the GUI, based on relativemagnitude of the first therapy score and the second therapy score.

In some embodiments, each of the plurality of biomarkers is selectedfrom the group consisting of: a genetic biomarker, a cellular biomarker,a saccharide biomarker, a lipid biomarker, a heterocyclic biomarker, anelementary compound biomarker, an imaging biomarker, an anthropologicalbiomarker, a personal habit biomarker, a disease-state biomarker, and anexpression biomarker.

In some embodiments, the value of one or more genetic biomarkers isdetermined through the identification of one or more mutations,insertions, deletions, rearrangements, fusions, copy number variations(CNV), or single nucleotide variants (SNV) in the nucleic acid orprotein of the genetic biomarker.

In some embodiments, the one or more genetic biomarkers includes a geneor marker described in the description and/or the figures.

In some embodiments, one or more genetic biomarkers are selected fromthe group consisting of: interferons, cytotoxic proteins, enzymes, celladhesion proteins, extracellular matrix proteins and polysaccharides,cell growth factors, cell differentiation factors, transcriptionfactors, and intracellular signaling proteins.

In some embodiments, the one or more genetic biomarkers is selected fromthe group consisting of: a cytokine, a chemokine, a chemokine receptor,and an interleukin.

In some embodiments, the value of one or more cellular biomarkers isdetermined through analysis of the number of one or more types of cellsor the percentage of one or more types of cells within the biologicalsample.

In some embodiments, the one or more types of cells are selected fromthe group consisting of malignant cancerous cells, leukocytes,lymphocytes, stromal cells, vascular endothelial cells, vascularpericytes, and myeloid-derived suppressor cells (MDSCs).

In some embodiments, the value of one or more expression biomarkers isdetermined through analysis of the expression level or enzymaticactivity of the nucleic acid or protein of the expression biomarker.

In some embodiments, the sequencing data is one or more of: DNAsequencing data, RNA sequencing data, or proteome sequencing data. Insome embodiments, the sequencing data is obtained using one or more ofthe following techniques: whole genome sequencing (WGS), whole exomesequencing (WES), whole transcriptome sequencing, mRNA sequencing,DNA/RNA-hybridization, microarray, DNA/RNA chip, PCR, and singlenucleotide polymorphism (SNP) genotyping.

In some embodiments, each of the at least one biological samples is abodily fluid, a cell sample, a liquid biopsy, or a tissue biopsy. Insome embodiments, the tissue biopsy comprises one or more samples fromone or more tumors or tissues known or suspected of having cancerouscells.

In some embodiments, the biomarker information also comprises resultsfrom one or more of the following types of analyses: blood analysis,cytometry analysis, histological analysis, immunohistological analysis,and patient history analysis.

In some embodiments, each of the therapies are selected from the groupconsisting of: surgery, radiation therapy, chemotherapy, immunotherapy,viral therapy, targeted therapy, hormone therapy, transplants,phototherapy, cryotherapy, and hyperthermia.

In some embodiments, each of the therapies are selected fromimmunotherapy and targeted therapy.

In some embodiments, the therapy scores are indicative of response ofthe subject to administration of one therapy in the plurality oftherapies. In some embodiments, the therapy scores are indicative ofresponse of the subject to administration of multiple therapies in theplurality of therapies.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker, across a respective group of people, in at least areference subset of the plurality of biomarkers, each of the pluralityof biomarkers being associated with at least one therapy in a pluralityof therapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized biomarker scores for a first setof biomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized biomarker scores for a secondset of biomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalizedbiomarker scores as input to a statistical model to obtain a firsttherapy score for the first therapy; providing the second set ofnormalized biomarker scores as input to the statistical model to obtaina second therapy score for the second therapy; wherein the plurality oftherapies comprise at least two therapies selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, and an anti-CD20 therapy, and wherein theplurality of biomarkers associated with each of the plurality oftherapies comprises at least three biomarkers selected from the group ofbiomarkers associated with the respective therapy in Table 2.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker, across arespective group of people, in at least a reference subset of theplurality of biomarkers, each of the plurality of biomarkers beingassociated with at least one therapy in a plurality of therapies;determining, using the sequencing data and the biomarker information: afirst set of normalized biomarker scores for a first set of biomarkersassociated with a first therapy in the plurality of therapies; and asecond set of normalized biomarker scores for a second set of biomarkersassociated with a second therapy in the plurality of therapies, whereinthe first set of biomarkers is different from the second set ofbiomarkers; providing the first set of normalized biomarker scores asinput to a statistical model to obtain a first therapy score for thefirst therapy; providing the second set of normalized biomarker scoresas input to the statistical model to obtain a second therapy score forthe second therapy; wherein the plurality of therapies comprise at leasttwo therapies selected from the group consisting of: an anti-PD1therapy, an anti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy,an anti-cancer vaccine therapy, an anti-angiogenic therapy, and ananti-CD20 therapy, and wherein the plurality of biomarkers associatedwith each of the plurality of therapies comprises at least threebiomarkers selected from the group of biomarkers associated with therespective therapy in Table 2.

In one aspect provided herein is a method, comprising using at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker, across a respective group of people, in at least areference subset of the plurality of biomarkers, each of the pluralityof biomarkers being associated with at least one therapy in a pluralityof therapies; determining, using the sequencing data and the biomarkerinformation: a first set of normalized biomarker scores for a first setof biomarkers associated with a first therapy in the plurality oftherapies; and a second set of normalized biomarker scores for a secondset of biomarkers associated with a second therapy in the plurality oftherapies, wherein the first set of biomarkers is different from thesecond set of biomarkers; providing the first set of normalizedbiomarker scores as input to a statistical model to obtain a firsttherapy score for the first therapy; providing the second set ofnormalized biomarker scores as input to the statistical model to obtaina second therapy score for the second therapy; wherein the plurality oftherapies comprise at least two therapies selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, and an anti-CD20 therapy, and wherein theplurality of biomarkers associated with each of the plurality oftherapies comprises at least three biomarkers selected from the group ofbiomarkers associated with the respective therapy in Table 2.

In some embodiments, the plurality of biomarkers includes a firstbiomarker, and wherein determining a normalized score for each biomarkerin at least the subject subset of the plurality of biomarkers comprises:determining a first normalized score for the first biomarker using thedistribution of values for the first biomarker. In some embodiments,determining the first normalized score comprises: determining anun-normalized score for the first biomarker using the sequencing data;determining a Z-score based on the first distribution of values for thefirst biomarker; and determining a normalized score for the firstbiomarker based on the un-normalized score and the Z-score.

In some embodiments, determining therapy scores for the plurality oftherapies comprises determining a first therapy score for a firsttherapy in the plurality of therapies as a sum of two or more scores inthe set of normalized biomarker scores for the subject.

In some embodiments, determining therapy scores for the plurality oftherapies comprises determining a first therapy score for a firsttherapy in the plurality of therapies at least in part by: determiningweights for two or more scores in the set of normalized biomarker scoresfor the subject; and determining the first therapy score as a sum of thetwo or more scores, summands of the sum being weighted by the determinedweights.

In some embodiments, determining the weights comprises determining theweights using a machine learning technique. In some embodiments,determining the weights comprises determining the weights using ageneralized linear model. In some embodiments, determining the weightscomprises determining the weights using a logistic regression model.

In some embodiments, the plurality of therapies comprises a firsttherapy and a second therapy different from the first therapy, andwherein determining therapy scores for the plurality of therapiescomprises: determining a first therapy score for the first therapy usinga first subset of the set of normalized biomarker scores for thesubject; and determining a second therapy score for the second therapyusing a second subset of the set of normalized biomarker scores for thesubject, wherein the second subset is different from the first subset.

Some embodiments include recommending at least one of the plurality oftherapies for the subject based on the determined therapy scores. Insome embodiments, recommending the at least one of the plurality oftherapies comprises: ranking the plurality of therapies based on thedetermined therapy scores; and recommending at least a threshold numberof top-ranked therapies for the subject.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-PD1 therapy in Table 2.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CTLA4 therapy in Table 2.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withIL-2 therapy in Table 2.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withIFN alpha therapy in Table 2.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-cancer vaccine therapy in Table 2.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-angiogenic therapy in Table 2.

In some embodiments, determining the normalized biomarker scores for thesubject comprises determining a normalized score for each of at leastthree biomarkers selected from the group of biomarkers associated withanti-CD20 therapy in Table 2. In some embodiments, the anti-CD20 therapyis rituximab.

Some embodiments include generating a graphical user interface (GUI)comprising: a first portion associated with a first therapy in theplurality of therapies, the first portion including a first plurality ofGUI elements, each of the first plurality of GUI elements beingassociated with a respective biomarker in the plurality of biomarkersand having at least one visual characteristic determined based on anormalized score of the respective biomarker in the first set ofnormalized scores; and a second portion associated with a second therapyin the plurality of therapies, the second portion including a secondplurality of GUI elements different from the first plurality of GUIelements, each of the second plurality of GUI elements being associatedwith a respective biomarker in the plurality of biomarkers and having atleast one visual characteristic determined based on a normalized scoreof the respective biomarker in the second set of normalized scores; anddisplaying the generated GUI.

In some embodiments, the at least one visual characteristic comprisescolor of a GUI element and/or size of the GUI element. Some embodimentsinclude in response to receiving, via the GUI, a user selection of thefirst therapy, presenting, via the GUI, information about at least onebiomarker with which at least one of the first plurality of GUI elementsis associated.

In some embodiments, the first therapy is associated with a firsttherapy score and the second therapy is associated with a second therapyscore, and wherein the first portion and the second portion arepositioned, relative to one another in the GUI, based on relativemagnitude of the first therapy score and the second therapy score.

In some embodiments, each of the plurality of biomarkers is selectedfrom the group consisting of: a genetic biomarker, a cellular biomarker,a saccharide biomarker, a lipid biomarker, a heterocyclic biomarker, anelementary compound biomarker, an imaging biomarker, an anthropologicalbiomarker, a personal habit biomarker, a disease-state biomarker, and anexpression biomarker.

In some embodiments, the value of one or more genetic biomarkers isdetermined through the identification of one or more mutations,insertions, deletions, rearrangements, fusions, copy number variations(CNV), or single nucleotide variants (SNV) in the nucleic acid orprotein of the genetic biomarker. In some embodiments, the one or moregenetic biomarkers includes a gene or marker described in thedescription and/or the figures. In some embodiments, one or more geneticbiomarkers are selected from the group consisting of: interferons,cytotoxic proteins, enzymes, cell adhesion proteins, extracellularmatrix proteins and polysaccharides, cell growth factors, celldifferentiation factors, transcription factors, and intracellularsignaling proteins. In some embodiments, the one or more geneticbiomarkers is selected from the group consisting of: a cytokine, achemokine, a chemokine receptor, and an interleukin.

In some embodiments, the value of one or more cellular biomarkers isdetermined through analysis of the number of one or more types of cellsor the percentage of one or more types of cells within the biologicalsample. In some embodiments, the one or more types of cells are selectedfrom the group consisting of malignant cancerous cells, leukocytes,lymphocytes, stromal cells, vascular endothelial cells, vascularpericytes, and myeloid-derived suppressor cells (MDSCs).

In some embodiments, the value of one or more expression biomarkers isdetermined through analysis of the expression level or enzymaticactivity of the nucleic acid or protein of the expression biomarker.

In some embodiments, the sequencing data is one or more of: DNAsequencing data, RNA sequencing data, or proteome sequencing data. Insome embodiments, the sequencing data is obtained using one or more ofthe following techniques: whole genome sequencing (WGS), whole exomesequencing (WES), whole transcriptome sequencing, mRNA sequencing,DNA/RNA-hybridization, microarray, DNA/RNA chip, PCR, and singlenucleotide polymorphism (SNP) genotyping.

In some embodiments, each of the at least one biological samples is abodily fluid, a cell sample, a liquid biopsy, or a tissue biopsy. Insome embodiments, the tissue biopsy comprises one or more samples fromone or more tumors or tissues known or suspected of having cancerouscells.

In some embodiments, the biomarker information also comprises resultsfrom one or more of the following types of analyses: blood analysis,cytometry analysis, histological analysis, immunohistological analysis,and patient history analysis.

In some embodiments, each of the therapies are selected from the groupconsisting of: surgery, radiation therapy, chemotherapy, immunotherapy,viral therapy, targeted therapy, hormone therapy, transplants,phototherapy, cryotherapy, and hyperthermia. In some embodiments, eachof the therapies are selected from immunotherapy and targeted therapy.

In some embodiments, the therapy scores are indicative of response ofthe subject to administration of one therapy in the plurality oftherapies. In some embodiments, the therapy scores are indicative ofresponse of the subject to administration of multiple therapies in theplurality of therapies.

In one aspect provided herein is a system, comprising: at least onecomputer hardware processor; at least one database that stores biomarkerinformation; and at least one non-transitory computer-readable storagemedium storing processor-executable instructions that, when executed bythe at least one computer hardware processor, cause the at least onecomputer hardware processor to perform: obtaining sequencing data aboutat least one biological sample of a subject; accessing, in the at leastone database, biomarker information indicating a distribution of valuesfor each biomarker, across a respective group of people, in at least areference subset of the plurality of biomarkers, each of the pluralityof biomarkers being associated with at least one candidate therapy;determining, using the sequencing data and the biomarker information, anormalized score for each biomarker in at least a subject subset of theplurality of biomarkers to obtain a set of normalized biomarkers for thesubject; identifying the subject as a member of one or more cohortsbased on the set of normalized biomarker scores for the subject, whereineach of the one or more cohorts is associated with a positive ornegative outcome of the at least one candidate therapy; and outputtingan indication of the one or more cohorts in which the subject is amember.

In one aspect provided herein is at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining sequencing data about at least one biological sampleof a subject; accessing, in at least one database, biomarker informationindicating a distribution of values for each biomarker, across arespective group of people, in at least a reference subset of theplurality of biomarkers, each of the plurality of biomarkers beingassociated with at least one candidate therapy; determining, using thesequencing data and the biomarker information, a normalized score foreach biomarker in at least a subject subset of the plurality ofbiomarkers to obtain a set of normalized biomarkers for the subject;identifying the subject as a member of one or more cohorts based on theset of normalized biomarker scores for the subject, wherein each of theone or more cohorts is associated with a positive or negative outcome ofthe at least one candidate therapy; and outputting an indication of theone or more cohorts in which the subject is a member.

In one aspect a method comprising using at least one computer hardwareprocessor to perform: obtaining sequencing data about at least onebiological sample of a subject; accessing, in at least one database,biomarker information indicating a distribution of values for eachbiomarker, across a respective group of people, in at least a referencesubset of the plurality of biomarkers, each of the plurality ofbiomarkers being associated with at least one candidate therapy;determining, using the sequencing data and the biomarker information, anormalized score for each biomarker in at least a subject subset of theplurality of biomarkers to obtain a set of normalized biomarkers for thesubject; identifying the subject as a member of one or more cohortsbased on the set of normalized biomarker scores for the subject, whereineach of the one or more cohorts is associated with a positive ornegative outcome of the at least one candidate therapy; and outputtingan indication of the one or more cohorts in which the subject is amember.

In some embodiments, the at least one candidate therapy is associatedwith a clinical trial, optionally wherein the clinical trial is ongoingor the clinical trial is recruiting.

In some embodiments, the positive outcome is an improvement in one ormore aspects of a cancer or in one or more cancer symptoms.

In some embodiments, the improvement in one or more aspects of a canceror one or more cancer symptoms is selected from the group consisting of:decrease in tumor size, decrease in tumor number, decrease in number orpercentage of cancerous cells in the body of the subject, and slowing ofcancer growth.

In some embodiments, the negative outcome is a cancer therapy-relatedadverse effect, an deterioration in one or more aspects of a cancer, ora deterioration in one or more cancer symptoms.

In some embodiments, the cancer therapy-related adverse effect isselected from: cutaneous toxicity, thrombocytopenia, hepatotoxicity,neurotoxicity, nephrotoxicity, cardiotoxicity, hemorrhagic cystitis,immune-related toxicity, and death.

In some embodiments, the deterioration in one or more aspects of acancer or one or more cancer symptoms is selected from the groupconsisting of: increase in tumor size, increase in tumor number,increase in number or percentage of cancerous cells in the body of thesubject, no slowing of cancer growth, and death.

In some embodiments, the sequencing data is one or more of: DNAsequencing data, RNA sequencing data, or proteome sequencing data. Insome embodiments, the sequencing data is obtained using one or more ofthe following techniques: whole genome sequencing (WGS), whole exomesequencing (WES), whole transcriptome sequencing, mRNA sequencing,DNA/RNA-hybridization, microarray, DNA/RNA chip, PCR, and singlenucleotide polymorphism (SNP) genotyping.

In some embodiments, the biological sample is from a tumor or tissueknown or suspected of having cancerous cells. In some embodiments, eachof the at least one biological samples is a bodily fluid, a cell sample,a liquid biopsy, or a tissue biopsy. In some embodiments, the biologicalsample is blood.

Some embodiments include generating a graphical user interface (GUI)comprising: a first portion associated with the at least one candidatetherapy, the first portion including a first plurality of GUI elements,each of the first plurality of GUI elements being associated with arespective biomarker in the plurality of biomarkers and having at leastone visual characteristic determined based on a difference score of therespective biomarker; and displaying the generated GUI. In someembodiments, the at least one visual characteristic comprises color of aGUI element and/or size of the GUI element. Some embodiments include inresponse to receiving, via the GUI, a user selection of the at least onecandidate therapy, presenting, via the GUI, information about at leastone biomarker with which at least one of the first plurality of GUIelements is associated.

EQUIVALENTS AND SCOPE

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of processor-executableinstructions that can be employed to program a computer or otherprocessor (physical or virtual) to implement various aspects ofembodiments as discussed above. Additionally, according to one aspect,one or more computer programs that when executed perform methods of thetechnology described herein need not reside on a single computer orprocessor, but may be distributed in a modular fashion among differentcomputers or processors to implement various aspects of the technologydescribed herein.

Processor-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed.

Also, data structures may be stored in one or more non-transitorycomputer-readable storage media in any suitable form. For simplicity ofillustration, data structures may be shown to have fields that arerelated through location in the data structure. Such relationships maylikewise be achieved by assigning storage for the fields with locationsin a non-transitory computer-readable medium that convey relationshipbetween the fields. However, any suitable mechanism may be used toestablish relationships among information in fields of a data structure,including through the use of pointers, tags or other mechanisms thatestablish relationships among data elements.

Various inventive concepts may be embodied as one or more processes, ofwhich examples have been provided. The acts performed as part of eachprocess may be ordered in any suitable way. Thus, embodiments may beconstructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, forexample, “at least one of A and B” (or, equivalently, “at least one of Aor B,” or, equivalently “at least one of A and/or B”) can refer, in oneembodiment, to at least one, optionally including more than one, A, withno B present (and optionally including elements other than B); inanother embodiment, to at least one, optionally including more than one,B, with no A present (and optionally including elements other than A);in yet another embodiment, to at least one, optionally including morethan one, A, and at least one, optionally including more than one, B(and optionally including other elements); etc.

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as an example, a reference to “A and/or B”, when used inconjunction with open-ended language such as “comprising” can refer, inone embodiment, to A only (optionally including elements other than B);in another embodiment, to B only (optionally including elements otherthan A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

In the claims articles such as “a,” “an,” and “the” may mean one or morethan one unless indicated to the contrary or otherwise evident from thecontext. Claims or descriptions that include “or” between one or moremembers of a group are considered satisfied if one, more than one, orall of the group members are present in, employed in, or otherwiserelevant to a given product or process unless indicated to the contraryor otherwise evident from the context. The disclosure includesembodiments in which exactly one member of the group is present in,employed in, or otherwise relevant to a given product or process. Thedisclosure includes embodiments in which more than one, or all of thegroup members are present in, employed in, or otherwise relevant to agiven product or process.

Furthermore, the described methods and systems encompass all variations,combinations, and permutations in which one or more limitations,elements, clauses, and descriptive terms from one or more of the listedclaims is introduced into another claim. For example, any claim that isdependent on another claim can be modified to include one or morelimitations found in any other claim that is dependent on the same baseclaim. Where elements are presented as lists, e.g., in Markush groupformat, each subgroup of the elements is also disclosed, and anyelement(s) can be removed from the group. It should it be understoodthat, in general, where the systems and methods described herein (oraspects thereof) are referred to as comprising particular elementsand/or features, certain embodiments of the systems and methods oraspects of the same consist, or consist essentially of, such elementsand/or features. For purposes of simplicity, those embodiments have notbeen specifically set forth in haec verba herein.

It is also noted that the terms “including,” “comprising,” “having,”“containing”, “involving”, are intended to be open and permits theinclusion of additional elements or steps. Where ranges are given,endpoints are included. Furthermore, unless otherwise indicated orotherwise evident from the context and understanding of one of ordinaryskill in the art, values that are expressed as ranges can assume anyspecific value or sub-range within the stated ranges in differentembodiments of the described systems and methods, to the tenth of theunit of the lower limit of the range, unless the context clearlydictates otherwise.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed. Such terms areused merely as labels to distinguish one claim element having a certainname from another element having a same name (but for use of the ordinalterm).

Additionally, as used herein the terms “patient” and “subject” may beused interchangeably. Such terms may include, but are not limited to,human subjects or patients. Such terms may also include non-humanprimates or other animals.

This application refers to various issued patents, published patentapplications, journal articles, and other publications, all of which areincorporated herein by reference. If there is a conflict between any ofthe incorporated references and the instant specification, thespecification shall control. In addition, any particular embodiment ofthe present disclosure that fall within the prior art may be explicitlyexcluded from any one or more of the claims. Because such embodimentsare deemed to be known to one of ordinary skill in the art, they may beexcluded even if the exclusion is not set forth explicitly herein. Anyparticular embodiment of the systems and methods described herein can beexcluded from any claim, for any reason, whether or not related to theexistence of prior art.

Those skilled in the art will recognize or be able to ascertain using nomore than routine experimentation many equivalents to the specificembodiments described herein. The scope of the present embodimentsdescribed herein is not intended to be limited to the above Description,but rather is as set forth in the appended claims. Those of ordinaryskill in the art will appreciate that various changes and modificationsto this description may be made without departing from the spirit orscope of the present disclosure, as defined in the following claims.

1. A method, comprising: using at least one computer hardware processorto perform: obtaining first sequencing data obtained by sequencing afirst biological sample of a subject having, suspected of having, or atrisk of having cancer, the first biological sample of the subjectobtained prior to administration of a therapy to the subject; obtainingsecond sequencing data by sequencing a second biological sample of thesubject, the second biological sample of the subject obtained subsequentto administration of the therapy to the subject; accessing, in at leastone database, biomarker information indicating a distribution of valuesfor each biomarker in a plurality of biomarkers, the plurality ofbiomarkers including a first biomarker associated with the therapy, thebiomarker information including a first distribution of values for thefirst biomarker; determining, using the first sequencing data, thesecond sequencing data, and the biomarker information, an impact scorefor the therapy, wherein the impact score is indicative of response ofthe subject to administration of the therapy, the determiningcomprising: determining a first set of normalized scores for a set ofbiomarkers associated with the therapy, the determining comprisingdetermining a first normalized score for the first biomarker at least inpart by determining a first statistical score for the first biomarkerbased on the first distribution of values for the first biomarker;determining a second set of normalized scores for the set of biomarkersassociated with the therapy, wherein the determining comprisesdetermining a second normalized score for the first biomarker at leastin part by determining a second statistical score for the firstbiomarker based on the first distribution of values for the firstbiomarker; and determining the impact score using the first set ofnormalized scores and the second set of normalized scores, wherein thetherapy is selected from the group consisting of: an anti-PD1 therapy,an anti-CTLA4 therapy, an IL-2 therapy, an IFN alpha therapy, ananti-cancer vaccine therapy, an anti-angiogenic therapy, an anti-CD20therapy, an immunotherapy, a T cell therapy, and a targeted therapy. 2.The method of claim 1, wherein determining the impact score using thefirst set of normalized scores and the second set of normalized scorescomprises: determining a difference score for each biomarker in the setof biomarkers to obtain a set of biomarker difference scores for thesubject; and determining the impact score using the set of biomarkerdifference scores.
 3. The method of claim 1, wherein determining theimpact score using the first set of normalized scores and the second setof normalized scores comprises: determining a first subject score forthe set of biomarkers using the first set of normalized biomarkerscores; determining a second subject score for the set of biomarkersusing the second set of normalized biomarker scores; and determining theimpact score based on a difference between the second subject score andthe first subject score.
 4. The method of claim 1, wherein the set ofbiomarkers comprises at least three biomarkers selected from the groupof biomarkers associated with the therapy in Table
 2. 5. The method ofclaim 1, wherein the set of biomarkers comprises at least fivebiomarkers selected from the group of biomarkers associated with thetherapy in Table
 2. 6. The method of claim 1, wherein the set ofbiomarkers comprises at least ten biomarkers selected from the group ofbiomarkers associated with the therapy in Table
 2. 7. The method ofclaim 1, wherein determining the first normalized score comprises:determining a first un-normalized score for the first biomarker usingthe first sequencing data; determining a first Z-score based on thefirst distribution of values for the first biomarker; and determiningthe first normalized score for the first biomarker based on the firstun-normalized score and the first Z-score.
 8. The method of claim 1,further comprising: administering the therapy to the subject subsequentto obtaining the first biological sample from the subject and prior toobtaining second biological sample of the subject.
 9. The method ofclaim 1, further comprising sequencing the first biological sample toobtain the first sequencing data.
 10. The method of claim 1, wherein theplurality of biomarkers includes the set of biomarkers associated withthe therapy.
 11. The method of claim 1, wherein the first set ofnormalized scores includes at least two scores and the second set ofnormalized scores includes at least two scores.
 12. The method of claim1, wherein the therapy is a checkpoint inhibitor therapy.
 13. A system,comprising: at least one computer hardware processor; and at least onenon-transitory computer-readable storage medium storing processorexecutable instructions that when executed by the at least one computerhardware processor, cause the computer hardware processor to perform:obtaining first sequencing data obtained by sequencing a firstbiological sample of a subject having, suspected of having, or at riskof having cancer, the first biological sample of the subject obtainedprior to administration of a therapy to the subject; obtaining secondsequencing data by sequencing a second biological sample of the subject,the second biological sample of the subject obtained subsequent toadministration of the therapy to the subject; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker in a plurality of biomarkers, the plurality of biomarkersincluding a first biomarker associated with the therapy, the biomarkerinformation including a first distribution of values for the firstbiomarker; determining, using the first sequencing data, the secondsequencing data, and the biomarker information, an impact score for thetherapy, wherein the impact score is indicative of response of thesubject to administration of the therapy, the determining comprising:determining a first set of normalized scores for a set of biomarkersassociated with the therapy, the determining comprising determining afirst normalized score for the first biomarker at least in part bydetermining a first statistical score for the first biomarker based onthe first distribution of values for the first biomarker; determining asecond set of normalized scores for the set of biomarkers associatedwith the therapy, wherein the determining comprises determining a secondnormalized score for the first biomarker at least in part by determininga second statistical score for the first biomarker based on the firstdistribution of values for the first biomarker; and determining theimpact score using the first set of normalized scores and the second setof normalized scores, wherein the therapy is selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, an anti-CD20 therapy, an immunotherapy, a Tcell therapy, and a targeted therapy.
 14. The system of claim 13,wherein determining the impact score using the first set of normalizedscores and the second set of normalized scores comprises: determining adifference score for each biomarker in the set of biomarkers to obtain aset of biomarker difference scores for the subject; and determining theimpact score using the set of biomarker difference scores.
 15. Thesystem of claim 13, wherein determining the impact score using the firstset of normalized scores and the second set of normalized scorescomprises: determining a first subject score for the set of biomarkersusing the first set of normalized biomarker scores; determining a secondsubject score for the set of biomarkers using the second set ofnormalized biomarker scores; and determining the impact score based on adifference between the second subject score and the first subject score.16. The system of claim 13, wherein the set of biomarkers comprises atleast three biomarkers selected from the group of biomarkers associatedwith the therapy in Table
 2. 17. The system of claim 13, wherein the setof biomarkers comprises at least ten biomarkers selected from the groupof biomarkers associated with the therapy in Table
 2. 18. The system ofclaim 13, wherein determining the first normalized score comprises:determining a first un-normalized score for the first biomarker usingthe first sequencing data; determining a first Z-score based on thefirst distribution of values for the first biomarker; and determiningthe first normalized score for the first biomarker based on the firstun-normalized score and the first Z-score.
 19. At least onenon-transitory computer-readable storage medium storing processorexecutable instructions that when executed by the at least one computerhardware processor, cause the computer hardware processor to perform:obtaining first sequencing data obtained by sequencing a firstbiological sample of a subject having, suspected of having, or at riskof having cancer, the first biological sample of the subject obtainedprior to administration of a therapy to the subject; obtaining secondsequencing data by sequencing a second biological sample of the subject,the second biological sample of the subject obtained subsequent toadministration of the therapy to the subject; accessing, in at least onedatabase, biomarker information indicating a distribution of values foreach biomarker in a plurality of biomarkers, the plurality of biomarkersincluding a first biomarker associated with the therapy, the biomarkerinformation including a first distribution of values for the firstbiomarker; determining, using the first sequencing data, the secondsequencing data, and the biomarker information, an impact score for thetherapy, wherein the impact score is indicative of response of thesubject to administration of the therapy, the determining comprising:determining a first set of normalized scores for a set of biomarkersassociated with the therapy, the determining comprising determining afirst normalized score for the first biomarker at least in part bydetermining a first statistical score for the first biomarker based onthe first distribution of values for the first biomarker; determining asecond set of normalized scores for the set of biomarkers associatedwith the therapy, wherein the determining comprises determining a secondnormalized score for the first biomarker at least in part by determininga second statistical score for the first biomarker based on the firstdistribution of values for the first biomarker; and determining theimpact score using the first set of normalized scores and the second setof normalized scores, wherein the therapy is selected from the groupconsisting of: an anti-PD1 therapy, an anti-CTLA4 therapy, an IL-2therapy, an IFN alpha therapy, an anti-cancer vaccine therapy, ananti-angiogenic therapy, an anti-CD20 therapy, an immunotherapy, a Tcell therapy, and a targeted therapy.
 20. The at least onenon-transitory computer-readable storage medium of claim 19, whereindetermining the impact score using the first set of normalized scoresand the second set of normalized scores comprises: determining adifference score for each biomarker in the set of biomarkers to obtain aset of biomarker difference scores for the subject; and determining theimpact score using the set of biomarker difference scores.
 21. The atleast one non-transitory computer-readable storage medium of claim 19,wherein determining the impact score using the first set of normalizedscores and the second set of normalized scores comprises: determining afirst subject score for the set of biomarkers using the first set ofnormalized biomarker scores; determining a second subject score for theset of biomarkers using the second set of normalized biomarker scores;and determining the impact score based on a difference between thesecond subject score and the first subject score.
 22. The at least onenon-transitory computer-readable storage medium of claim 19, wherein theset of biomarkers comprises at least three biomarkers selected from thegroup of biomarkers associated with the therapy in Table
 2. 23. The atleast one non-transitory computer-readable storage medium of claim 19,wherein the set of biomarkers comprises at least ten biomarkers selectedfrom the group of biomarkers associated with the therapy in Table
 2. 24.The at least one non-transitory computer-readable storage medium ofclaim 19, wherein determining the first normalized score comprises:determining a first un-normalized score for the first biomarker usingthe first sequencing data; determining a first Z-score based on thefirst distribution of values for the first biomarker; and determiningthe first normalized score for the first biomarker based on the firstun-normalized score and the first Z-score.